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datar.core.grouped

module

datar.core.grouped

Provide DataFrameGroupBy and DataFrameRowwise

Classes
abstract class

datar.core.grouped.DataFrameGroupByABC(data, _group_vars=None, _group_drop=None, _group_data=None, **kwargs)

Bases
pandas.core.frame.DataFrame pandas.core.generic.NDFrame pandas.core.base.PandasObject pandas.core.accessor.DirNamesMixin pandas.core.indexing.IndexingMixin pandas.core.arraylike.OpsMixin

Abstract class for DataFrameGroupBy

Attributes
  • at (_AtIndexer) Access a single value for a row/column label pair.
    Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series. </>
  • attrs (dict) Dictionary of global attributes of this dataset.
    .. warning::
    attrs is experimental and may change without warning. </>
  • axes (list) Return a list representing the axes of the DataFrame.
    It has the row axis labels and column axis labels as the only members. They are returned in that order. </>
  • dtypes Return the dtypes in the DataFrame.
    This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the object dtype. See :ref:the User Guide <basics.dtypes> for more. </>
  • empty Indicator whether DataFrame is empty.
    True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0. </>
  • flags (Flags) Get the properties associated with this pandas object.
    The available flags are
    • :attr:Flags.allows_duplicate_labels
    </>
  • iat (_iAtIndexer) Access a single value for a row/column pair by integer position.
    Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series. </>
  • iloc (_iLocIndexer) Purely integer-location based indexing for selection by position.
    .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.
    Allowed inputs are:
    • An integer, e.g. 5.
    • A list or array of integers, e.g. [4, 3, 0].
    • A slice object with ints, e.g. 1:7.
    • A boolean array.
    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don't have a reference to the calling object, but would like to base your selection on some value.
    .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).
    See more at :ref:Selection by Position <indexing.integer>. </>
  • loc (_LocIndexer) Access a group of rows and columns by label(s) or a boolean array.
    .loc[] is primarily label based, but may also be used with a boolean array.
    Allowed inputs are:
    • A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
    • A list or array of labels, e.g. ['a', 'b', 'c'].
    • A slice object with labels, e.g. 'a':'f'.
    .. warning:: Note that contrary to usual python slices, both the start and the stop are included
    • A boolean array of the same length as the axis being sliced, e.g. [True, False, True].
    • An alignable boolean Series. The index of the key will be aligned before masking.
    • An alignable Index. The Index of the returned selection will be the input.
    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)
    See more at :ref:Selection by Label <indexing.label>. </>
  • ndim (int) Return an int representing the number of axes / array dimensions.
    Return 1 if Series. Otherwise return 2 if DataFrame. </>
  • shape (tuple) Return a tuple representing the dimensionality of the DataFrame.</>
  • size (int) Return an int representing the number of elements in this object.
    Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. </>
  • style (Styler) Returns a Styler object.
    Contains methods for building a styled HTML representation of the DataFrame. </>
  • values Return a Numpy representation of the DataFrame.
    .. warning::
    We recommend using :meth:DataFrame.to_numpy instead.
    Only the values in the DataFrame will be returned, the axes labels will be removed. </>
Methods
  • __array_wrap__(result, context) Gets called after a ufunc and other functions.</>
  • __contains__(key) (bool) True if the key is in the info axis</>
  • __delitem__(key) Delete item</>
  • __dir__() (list) Provide method name lookup and completion.</>
  • __finalize__(other, method, **kwargs) (FrameOrSeries) Propagate metadata from other to self.</>
  • __getattr__(name) After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use. </>
  • __iter__() (iterator) Iterate over info axis.</>
  • __len__() (int) Returns length of info axis, but here we use the index.</>
  • __matmul__(other) (DataFrame or Series) Matrix multiplication using binary @ operator in Python>=3.5.</>
  • __repr__() (str) Return a string representation for a particular DataFrame.</>
  • __rmatmul__(other) Matrix multiplication using binary @ operator in Python>=3.5.</>
  • __setattr__(name, value) After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use. </>
  • __sizeof__() (int) Generates the total memory usage for an object that returnseither a value or Series of values </>
  • abs() (abs) Return a Series/DataFrame with absolute numeric value of each element.</>
  • add_prefix(prefix) (Series or DataFrame) Prefix labels with string prefix.</>
  • add_suffix(suffix) (Series or DataFrame) Suffix labels with string suffix.</>
  • aggregate(func, axis, *args, **kwargs) (scalar, Series or DataFrame) Aggregate using one or more operations over the specified axis.</>
  • align(other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis) (left, right) : (DataFrame, type of other) Align two objects on their axes with the specified join method.</>
  • all(axis, bool_only, skipna, level, **kwargs) (Series or DataFrame) Return whether all elements are True, potentially over an axis.</>
  • any(axis, bool_only, skipna, level, **kwargs) (Series or DataFrame) Return whether any element is True, potentially over an axis.</>
  • append(other, ignore_index, verify_integrity, sort) (DataFrame) Append rows of other to the end of caller, returning a new object.</>
  • apply(func, axis, raw, result_type, args, **kwargs) (Series or DataFrame) Apply a function along an axis of the DataFrame.</>
  • applymap(func, na_action, **kwargs) (DataFrame) Apply a function to a Dataframe elementwise.</>
  • asfreq(freq, method, how, normalize, fill_value) (DataFrame) Convert time series to specified frequency.</>
  • asof(where, subset) (scalar, Series, or DataFrame) Return the last row(s) without any NaNs before where.</>
  • assign(**kwargs) (DataFrame) Assign new columns to a DataFrame.</>
  • astype(dtype, copy, errors) (casted : same type as caller) Cast a pandas object to a specified dtype dtype.</>
  • at_time(time, asof, axis) (Series or DataFrame) Select values at particular time of day (e.g., 9:30AM).</>
  • between_time(start_time, end_time, include_start, include_end, axis) (Series or DataFrame) Select values between particular times of the day (e.g., 9:00-9:30 AM).</>
  • bfill(axis, inplace, limit, downcast) (Series/DataFrame or None) Synonym for :meth:DataFrame.fillna with method='bfill'.</>
  • bool() (bool) Return the bool of a single element Series or DataFrame.</>
  • combine(other, func, fill_value, overwrite) (DataFrame) Perform column-wise combine with another DataFrame.</>
  • combine_first(other) (DataFrame) Update null elements with value in the same location in other.</>
  • compare(other, align_axis, keep_shape, keep_equal) (DataFrame) Compare to another DataFrame and show the differences.</>
  • convert_dtypes(infer_objects, convert_string, convert_integer, convert_boolean, convert_floating) (Series or DataFrame) Convert columns to best possible dtypes using dtypes supporting pd.NA.</>
  • copy(deep, copy_grouped) (DataFrameGroupByABC) Copy the dataframe and keep the class</>
  • corr(method, min_periods) (DataFrame) Compute pairwise correlation of columns, excluding NA/null values.</>
  • corrwith(other, axis, drop, method) (Series) Compute pairwise correlation.</>
  • count(axis, level, numeric_only) (Series or DataFrame) Count non-NA cells for each column or row.</>
  • cov(min_periods, ddof) (DataFrame) Compute pairwise covariance of columns, excluding NA/null values.</>
  • cummax(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative maximum over a DataFrame or Series axis.</>
  • cummin(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative minimum over a DataFrame or Series axis.</>
  • cumprod(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative product over a DataFrame or Series axis.</>
  • cumsum(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative sum over a DataFrame or Series axis.</>
  • describe(percentiles, include, exclude, datetime_is_numeric) (Series or DataFrame) Generate descriptive statistics.</>
  • diff(periods, axis) (Dataframe) First discrete difference of element.</>
  • dot(other) (Series or DataFrame) Compute the matrix multiplication between the DataFrame and other.</>
  • drop(labels, axis, index, columns, level, inplace, errors) (DataFrame or None) Drop specified labels from rows or columns.</>
  • drop_duplicates(subset, keep, inplace, ignore_index) (DataFrame or None) Return DataFrame with duplicate rows removed.</>
  • droplevel(level, axis) (Series/DataFrame) Return Series/DataFrame with requested index / column level(s) removed.</>
  • dropna(axis, how, thresh, subset, inplace) (DataFrame or None) Remove missing values.</>
  • duplicated(subset, keep) (Series) Return boolean Series denoting duplicate rows.</>
  • equals(other) (bool) Test whether two objects contain the same elements.</>
  • eval(expr, inplace, **kwargs) (ndarray, scalar, pandas object, or None) Evaluate a string describing operations on DataFrame columns.</>
  • ewm(com, span, halflife, alpha, min_periods, adjust, ignore_na, axis, times) (DataFrame) Provide exponential weighted (EW) functions.</>
  • expanding(min_periods, center, axis, method) (a Window sub-classed for the particular operation) Provide expanding transformations.</>
  • explode(column, ignore_index) (DataFrame) Transform each element of a list-like to a row, replicating index values.</>
  • ffill(axis, inplace, limit, downcast) (Series/DataFrame or None) Synonym for :meth:DataFrame.fillna with method='ffill'.</>
  • fillna(value, method, axis, inplace, limit, downcast) (DataFrame or None) Fill NA/NaN values using the specified method.</>
  • filter(items, like, regex, axis) (same type as input object) Subset the dataframe rows or columns according to the specified index labels.</>
  • first(offset) (Series or DataFrame) Select initial periods of time series data based on a date offset.</>
  • first_valid_index() (scalar : type of index) Return index for first non-NA value or None, if no NA value is found.</>
  • from_dict(data, orient, dtype, columns) (DataFrame) Construct DataFrame from dict of array-like or dicts.</>
  • from_records(data, index, exclude, columns, coerce_float, nrows) (DataFrame) Convert structured or record ndarray to DataFrame.</>
  • get(key, default) (value : same type as items contained in object) Get item from object for given key (ex: DataFrame column).</>
  • groupby(by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna) (DataFrameGroupBy) Group DataFrame using a mapper or by a Series of columns.</>
  • head(n) (same type as caller) Return the first n rows.</>
  • idxmax(axis, skipna) (Series) Return index of first occurrence of maximum over requested axis.</>
  • idxmin(axis, skipna) (Series) Return index of first occurrence of minimum over requested axis.</>
  • infer_objects() (converted : same type as input object) Attempt to infer better dtypes for object columns.</>
  • info(verbose, buf, max_cols, memory_usage, show_counts, null_counts) (None) Print a concise summary of a DataFrame.</>
  • insert(loc, column, value, allow_duplicates) Insert column into DataFrame at specified location.</>
  • isin(values) (DataFrame) Whether each element in the DataFrame is contained in values.</>
  • isna() (DataFrame) Detect missing values.</>
  • isnull() (DataFrame) Detect missing values.</>
  • items() (label : object) Iterate over (column name, Series) pairs.</>
  • iteritems() (label : object) Iterate over (column name, Series) pairs.</>
  • iterrows() (index : label or tuple of label) Iterate over DataFrame rows as (index, Series) pairs.</>
  • itertuples(index, name) (iterator) Iterate over DataFrame rows as namedtuples.</>
  • join(other, on, how, lsuffix, rsuffix, sort) (DataFrame) Join columns of another DataFrame.</>
  • keys() (Index) Get the 'info axis' (see Indexing for more).</>
  • kurt(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased kurtosis over requested axis.</>
  • last(offset) (Series or DataFrame) Select final periods of time series data based on a date offset.</>
  • last_valid_index() (scalar : type of index) Return index for last non-NA value or None, if no NA value is found.</>
  • lookup(row_labels, col_labels) (numpy.ndarray) Label-based "fancy indexing" function for DataFrame.Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. </>
  • mad(axis, skipna, level) (Series or DataFrame (if level specified)) Return the mean absolute deviation of the values over the requested axis.</>
  • max(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the maximum of the values over the requested axis.</>
  • mean(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the mean of the values over the requested axis.</>
  • median(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the median of the values over the requested axis.</>
  • melt(id_vars, value_vars, var_name, value_name, col_level, ignore_index) (DataFrame) Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.</>
  • memory_usage(index, deep) (Series) Return the memory usage of each column in bytes.</>
  • merge(right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate) (DataFrame) Merge DataFrame or named Series objects with a database-style join.</>
  • min(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the minimum of the values over the requested axis.</>
  • mode(axis, numeric_only, dropna) (DataFrame) Get the mode(s) of each element along the selected axis.</>
  • nlargest(n, columns, keep) (DataFrame) Return the first n rows ordered by columns in descending order.</>
  • notna() (DataFrame) Detect existing (non-missing) values.</>
  • notnull() (DataFrame) Detect existing (non-missing) values.</>
  • nsmallest(n, columns, keep) (DataFrame) Return the first n rows ordered by columns in ascending order.</>
  • nunique(axis, dropna) (Series) Count number of distinct elements in specified axis.</>
  • pct_change(periods, fill_method, limit, freq, **kwargs) (chg : Series or DataFrame) Percentage change between the current and a prior element.</>
  • pipe(func, *args, **kwargs) (object : the return type of ``func``.) Apply func(self, *args, **kwargs).</>
  • pivot(index, columns, values) (DataFrame) Return reshaped DataFrame organized by given index / column values.</>
  • pivot_table(values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort) (DataFrame) Create a spreadsheet-style pivot table as a DataFrame.</>
  • pop(item) (Series) Return item and drop from frame. Raise KeyError if not found.</>
  • prod(axis, skipna, level, numeric_only, min_count, **kwargs) (Series or DataFrame (if level specified)) Return the product of the values over the requested axis.</>
  • quantile(q, axis, numeric_only, interpolation) (Series or DataFrame) Return values at the given quantile over requested axis.</>
  • query(expr, inplace, **kwargs) (DataFrame or None) Query the columns of a DataFrame with a boolean expression.</>
  • rank(axis, method, numeric_only, na_option, ascending, pct) (same type as caller) Compute numerical data ranks (1 through n) along axis.</>
  • reindex_like(other, method, copy, limit, tolerance) (Series or DataFrame) Return an object with matching indices as other object.</>
  • rename(mapper, index, columns, axis, copy, inplace, level, errors) (DataFrame or None) Alter axes labels.</>
  • rename_axis(mapper, index, columns, axis, copy, inplace) (Series, DataFrame, or None) Set the name of the axis for the index or columns.</>
  • reorder_levels(order, axis) (DataFrame) Rearrange index levels using input order. May not drop or duplicate levels.</>
  • replace(to_replace, value, inplace, limit, regex, method) (DataFrame) Replace values given in to_replace with value.</>
  • resample(rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset) (pandas.core.Resampler) Resample time-series data.</>
  • reset_index(level, drop, inplace, col_level, col_fill) (DataFrame or None) Reset the index, or a level of it.</>
  • rolling(window, min_periods, center, win_type, on, axis, closed, method) (a Window or Rolling sub-classed for the particular operation) Provide rolling window calculations.</>
  • round(decimals, *args, **kwargs) (DataFrame) Round a DataFrame to a variable number of decimal places.</>
  • sample(n, frac, replace, weights, random_state, axis, ignore_index) (Series or DataFrame) Return a random sample of items from an axis of object.</>
  • select_dtypes(include, exclude) (DataFrame) Return a subset of the DataFrame's columns based on the column dtypes.</>
  • sem(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased standard error of the mean over requested axis.</>
  • set_axis(labels, axis, inplace) (renamed : DataFrame or None) Assign desired index to given axis.</>
  • set_flags(copy, allows_duplicate_labels) (Series or DataFrame) Return a new object with updated flags.</>
  • set_index(keys, drop, append, inplace, verify_integrity) (DataFrame or None) Set the DataFrame index using existing columns.</>
  • shift(periods, freq, axis, fill_value) (DataFrame) Shift index by desired number of periods with an optional time freq.</>
  • skew(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased skew over requested axis.</>
  • slice_shift(periods, axis) (shifted : same type as caller) Equivalent to shift without copying data.The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. </>
  • sort_index(axis, level, ascending, inplace, kind, na_position, sort_remaining, ignore_index, key) (DataFrame or None) Sort object by labels (along an axis).</>
  • sort_values(by, axis, ascending, inplace, kind, na_position, ignore_index, key) (DataFrame or None) Sort by the values along either axis.</>
  • squeeze(axis) (DataFrame, Series, or scalar) Squeeze 1 dimensional axis objects into scalars.</>
  • stack(level, dropna) (DataFrame or Series) Stack the prescribed level(s) from columns to index.</>
  • std(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return sample standard deviation over requested axis.</>
  • sum(axis, skipna, level, numeric_only, min_count, **kwargs) (Series or DataFrame (if level specified)) Return the sum of the values over the requested axis.</>
  • swapaxes(axis1, axis2, copy) (y : same as input) Interchange axes and swap values axes appropriately.</>
  • swaplevel(i, j, axis) (DataFrame) Swap levels i and j in a :class:MultiIndex.</>
  • tail(n) (type of caller) Return the last n rows.</>
  • take(indices, axis, is_copy, **kwargs) (taken : same type as caller) Return the elements in the given positional indices along an axis.</>
  • to_clipboard(excel, sep, **kwargs) Copy object to the system clipboard.</>
  • to_csv(path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options) (None or str) Write object to a comma-separated values (csv) file.</>
  • to_dict(orient, into) (dict, list or collections.abc.Mapping) Convert the DataFrame to a dictionary.</>
  • to_excel(excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes, storage_options) Write object to an Excel sheet.</>
  • to_feather(path, **kwargs) Write a DataFrame to the binary Feather format.</>
  • to_gbq(destination_table, project_id, chunksize, reauth, if_exists, auth_local_webserver, table_schema, location, progress_bar, credentials) Write a DataFrame to a Google BigQuery table.</>
  • to_hdf(path_or_buf, key, mode, complevel, complib, append, format, index, min_itemsize, nan_rep, dropna, data_columns, errors, encoding) Write the contained data to an HDF5 file using HDFStore.</>
  • to_html(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, max_cols, show_dimensions, decimal, bold_rows, classes, escape, notebook, border, table_id, render_links, encoding) (str or None) Render a DataFrame as an HTML table.</>
  • to_json(path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options) (None or str) Convert the object to a JSON string.</>
  • to_latex(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, bold_rows, column_format, longtable, escape, encoding, decimal, multicolumn, multicolumn_format, multirow, caption, label, position) Render object to a LaTeX tabular, longtable, or nested table/tabular.</>
  • to_markdown(buf, mode, index, storage_options, **kwargs) (str) Print DataFrame in Markdown-friendly format.</>
  • to_numpy(dtype, copy, na_value) (numpy.ndarray) Convert the DataFrame to a NumPy array.</>
  • to_parquet(path, engine, compression, index, partition_cols, storage_options, **kwargs) (bytes if no path argument is provided else None) Write a DataFrame to the binary parquet format.</>
  • to_period(freq, axis, copy) (DataFrame with PeriodIndex) Convert DataFrame from DatetimeIndex to PeriodIndex.</>
  • to_pickle(path, compression, protocol, storage_options) Pickle (serialize) object to file.</>
  • to_records(index, column_dtypes, index_dtypes) (numpy.recarray) Convert DataFrame to a NumPy record array.</>
  • to_sql(name, con, schema, if_exists, index, index_label, chunksize, dtype, method) Write records stored in a DataFrame to a SQL database.</>
  • to_stata(path, convert_dates, write_index, byteorder, time_stamp, data_label, variable_labels, version, convert_strl, compression, storage_options) Export DataFrame object to Stata dta format.</>
  • to_string(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, min_rows, max_cols, show_dimensions, decimal, line_width, max_colwidth, encoding) (str or None) Render a DataFrame to a console-friendly tabular output.</>
  • to_timestamp(freq, how, axis, copy) (DataFrame with DatetimeIndex) Cast to DatetimeIndex of timestamps, at beginning of period.</>
  • to_xarray() (xarray.DataArray or xarray.Dataset) Return an xarray object from the pandas object.</>
  • to_xml(path_or_buffer, index, root_name, row_name, na_rep, attr_cols, elem_cols, namespaces, prefix, encoding, xml_declaration, pretty_print, parser, stylesheet, compression, storage_options) (None or str) Render a DataFrame to an XML document.</>
  • transform(func, axis, *args, **kwargs) (DataFrame) Call func on self producing a DataFrame with transformed values.</>
  • transpose(*args, copy) (DataFrame) Transpose index and columns.</>
  • truncate(before, after, axis, copy) (type of caller) Truncate a Series or DataFrame before and after some index value.</>
  • tshift(periods, freq, axis) (shifted : Series/DataFrame) Shift the time index, using the index's frequency if available.</>
  • tz_convert(tz, axis, level, copy) ({klass}) Convert tz-aware axis to target time zone.</>
  • tz_localize(tz, axis, level, copy, ambiguous, nonexistent) (Series or DataFrame) Localize tz-naive index of a Series or DataFrame to target time zone.</>
  • unstack(level, fill_value) (Series or DataFrame) Pivot a level of the (necessarily hierarchical) index labels.</>
  • update(other, join, overwrite, filter_func, errors) (None : method directly changes calling object) Modify in place using non-NA values from another DataFrame.</>
  • value_counts(subset, normalize, sort, ascending, dropna) (Series) Return a Series containing counts of unique rows in the DataFrame.</>
  • var(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased variance over requested axis.</>
  • xs(key, axis, level, drop_level) (Series or DataFrame) Return cross-section from the Series/DataFrame.</>
method

__dir__() → list

Provide method name lookup and completion.

Notes

Only provide 'public' methods.

method

__sizeof__() → int

Generates the total memory usage for an object that returnseither a value or Series of values

method

set_flags(copy=False, allows_duplicate_labels=None)

Return a new object with updated flags.

Parameters
  • allows_duplicate_labels (bool, optional) Whether the returned object allows duplicate labels.
Returns (Series or DataFrame)

The same type as the caller.

See Also

DataFrame.attrs : Global metadata applying to this dataset.DataFrame.flags : Global flags applying to this object.

Notes

This method returns a new object that's a view on the same data as the input. Mutating the input or the output values will be reflected in the other.

This method is intended to be used in method chains.

"Flags" differ from "metadata". Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in :attr:DataFrame.attrs.

Examples
>>> df = pd.DataFrame({"A": [1, 2]})>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False
method

swapaxes(axis1, axis2, copy=True)

Interchange axes and swap values axes appropriately.

method

droplevel(level, axis=0)

Return Series/DataFrame with requested index / column level(s) removed.

Parameters
  • level (int, str, or list-like) If a string is given, must be the name of a levelIf list-like, elements must be names or positional indexes of levels.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis along which the level(s) is removed:
    • 0 or 'index': remove level(s) in column.
    • 1 or 'columns': remove level(s) in row.
Returns (Series/DataFrame)

Series/DataFrame with requested index / column level(s) removed.

Examples
>>> df = pd.DataFrame([...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
...     ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1   c   d
level_2   e   f
a b
1 2      3   4
5 6      7   8
9 10    11  12
>>> df.droplevel('a')
level_1   c   d
level_2   e   f
b
2        3   4
6        7   8
10      11  12
>>> df.droplevel('level_2', axis=1)
level_1   c   d
a b
1 2      3   4
5 6      7   8
9 10    11  12
method

squeeze(axis=None)

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don't know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default None) A specific axis to squeeze. By default, all length-1 axes aresqueezed.
Returns (DataFrame, Series, or scalar)

The projection after squeezing axis or all the axes.

See Also

Series.iloc : Integer-location based indexing for selecting scalars.DataFrame.iloc : Integer-location based indexing for selecting Series. Series.to_frame : Inverse of DataFrame.squeeze for a single-column DataFrame.

Examples
>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0    2
dtype: int64
>>> even_primes.squeeze()
2

Squeezing objects with more than one value in every axis does nothing:

>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1    3
2    5
3    7
dtype: int64
>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

>>> df_a = df[['a']]
>>> df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

>>> df_a.squeeze('columns')
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

>>> df_0a.squeeze('rows')
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_0a.squeeze()
1
method

rename_axis(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False)

Set the name of the axis for the index or columns.

Parameters
  • mapper (scalar, list-like, optional) Value to set the axis name attribute.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to rename.
  • copy (bool, default True) Also copy underlying data.
  • inplace (bool, default False) Modifies the object directly, instead of creating a new Seriesor DataFrame.
  • index, columns : scalar, list-like, dict-like or function, optional A scalar, list-like, dict-like or functions transformations toapply to that axis' values. Note that the columns parameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects.
    Use either mapper and axis to specify the axis to target with mapper, or index and/or columns.
Returns (Series, DataFrame, or None)

The same type as the caller or None if inplace=True.

See Also

Series.rename : Alter Series index labels or name.DataFrame.rename : Alter DataFrame index labels or name. Index.rename : Set new names on index.

Notes

DataFrame.rename_axis supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)
  • (mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

Series

>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0       dog
1       cat
2    monkey
dtype: object
>>> s.rename_axis("animal")
animal
0    dog
1    cat
2    monkey
dtype: object

DataFrame

>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
...                    "num_arms": [0, 0, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product([['mammal'],
...                                        ['dog', 'cat', 'monkey']],
...                                       names=['type', 'name'])
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(index={'type': 'class'})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
method

equals(other)

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype.

Parameters
  • other (Series or DataFrame) The other Series or DataFrame to be compared with the first.
Returns (bool)

True if all elements are the same in both objects, Falseotherwise.

See Also

Series.eq : Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise. DataFrame.eq : Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise. testing.assert_series_equal : Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others. testing.assert_frame_equal : Like assert_series_equal, but targets DataFrames. numpy.array_equal : Return True if two arrays have the same shape and elements, False otherwise.

Examples
>>> df = pd.DataFrame({1: [10], 2: [20]})>>> df
    1   2
0  10  20

DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.

>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.

>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
      1     2
0  10.0  20.0
>>> df.equals(different_data_type)
False
method

bool()

Return the bool of a single element Series or DataFrame.

This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).

Returns (bool)

The value in the Series or DataFrame.

See Also

Series.astype : Change the data type of a Series, including to boolean.DataFrame.astype : Change the data type of a DataFrame, including to boolean. numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.

Examples

The method will only work for single element objects with a boolean value:

>>> pd.Series([True]).bool()
True
>>> pd.Series([False]).bool()
False
>>> pd.DataFrame({'col': [True]}).bool()
True
>>> pd.DataFrame({'col': [False]}).bool()
False
method

__iter__()

Iterate over info axis.

Returns (iterator)

Info axis as iterator.

method

keys()

Get the 'info axis' (see Indexing for more).

This is index for Series, columns for DataFrame.

Returns (Index)

Info axis.

method

__contains__(key) → bool

True if the key is in the info axis

method

__array_wrap__(result, context=None)

Gets called after a ufunc and other functions.

Parameters
  • result (np.ndarray) The result of the ufunc or other function called on the NumPy arrayreturned by array
  • context (tuple of (func, tuple, int)) This parameter is returned by ufuncs as a 3-element tuple: (name of theufunc, arguments of the ufunc, domain of the ufunc), but is not set by other numpy functions.q

Notes

Series implements __array_ufunc_ so this not called for ufunc on Series.

method

to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None, storage_options=None)

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters
  • excel_writer (path-like, file-like, or ExcelWriter object) File path or existing ExcelWriter.
  • sheet_name (str, default 'Sheet1') Name of sheet which will contain DataFrame.
  • na_rep (str, default '') Missing data representation.
  • float_format (str, optional) Format string for floating point numbers. For examplefloat_format="%.2f" will format 0.1234 to 0.12.
  • columns (sequence or list of str, optional) Columns to write.
  • header (bool or list of str, default True) Write out the column names. If a list of string is given it isassumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • index_label (str or sequence, optional) Column label for index column(s) if desired. If not specified, andheader and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
  • startrow (int, default 0) Upper left cell row to dump data frame.
  • startcol (int, default 0) Upper left cell column to dump data frame.
  • engine (str, optional) Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set thisvia the options io.excel.xlsx.writer, io.excel.xls.writer, and io.excel.xlsm.writer.
    .. deprecated:: 1.2.0
    As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
    maintained, the ``xlwt`` engine will be removed in a future version
    of pandas.
    
  • merge_cells (bool, default True) Write MultiIndex and Hierarchical Rows as merged cells.
  • encoding (str, optional) Encoding of the resulting excel file. Only necessary for xlwt,other writers support unicode natively.
  • inf_rep (str, default 'inf') Representation for infinity (there is no native representation forinfinity in Excel).
  • verbose (bool, default True) Display more information in the error logs.
  • freeze_panes (tuple of int (length 2), optional) Specifies the one-based bottommost row and rightmost column thatis to be frozen.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
See Also

to_csv : Write DataFrame to a comma-separated values (csv) file.ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame.

Notes

For compatibility with :meth:~DataFrame.to_csv, to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.

Examples

:

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:

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s :

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method

to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=True, indent=None, storage_options=None)

Convert the object to a JSON string.

Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters
  • path_or_buf (str or file handle, optional) File path or object. If not specified, the result is returned asa string.
  • orient (str) Indication of expected JSON string format.
    • Series:
      • default is 'index'
      • allowed values are: {'split', 'records', 'index', 'table'}.
    • DataFrame:
      • default is 'columns'
      • allowed values are: {'split', 'records', 'index', 'columns', 'values', 'table'}.
    • The format of the JSON string:
      • 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
      • 'records' : list like [{column -> value}, ... , {column -> value}]
      • 'index' : dict like {index -> {column -> value}}
      • 'columns' : dict like {column -> {index -> value}}
      • 'values' : just the values array
      • 'table' : dict like {'schema': {schema}, 'data': {data}}
      Describing the data, where data component is like orient='records'.
  • date_format ({None, 'epoch', 'iso'}) Type of date conversion. 'epoch' = epoch milliseconds,'iso' = ISO8601. The default depends on the orient. For orient='table', the default is 'iso'. For all other orients, the default is 'epoch'.
  • double_precision (int, default 10) The number of decimal places to use when encodingfloating point values.
  • force_ascii (bool, default True) Force encoded string to be ASCII.
  • date_unit (str, default 'ms' (milliseconds)) The time unit to encode to, governs timestamp and ISO8601precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively.
  • default_handler (callable, default None) Handler to call if object cannot otherwise be converted to asuitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
  • lines (bool, default False) If 'orient' is 'records' write out line-delimited json format. Willthrow ValueError if incorrect 'orient' since others are not list-like.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}) ,e .
  • index (bool, default True) Whether to include the index values in the JSON string. Notincluding the index (index=False) is only supported when orient is 'split' or 'table'.
  • indent (int, optional) Length of whitespace used to indent each record.
    .. versionadded:: 1.0.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
Returns (None or str)

If path_or_buf is None, returns the resulting json format as astring. Otherwise returns None.

See Also

read_json : Convert a JSON string to pandas object.

Notes

The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release.

orient='table' contains a 'pandas_version' field under 'schema'. This stores the version of pandas used in the latest revision of the schema.

Examples
>>> import json>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> result = df.to_json(orient="records")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> result = df.to_json(orient="index")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "row 1": {
        "col 1": "a",
        "col 2": "b"
    },
    "row 2": {
        "col 1": "c",
        "col 2": "d"
    }
}

Encoding/decoding a Dataframe using 'columns' formatted JSON:

>>> result = df.to_json(orient="columns")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "col 1": {
        "row 1": "a",
        "row 2": "c"
    },
    "col 2": {
        "row 1": "b",
        "row 2": "d"
    }
}

Encoding/decoding a Dataframe using 'values' formatted JSON:

>>> result = df.to_json(orient="values")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
[
    [
        "a",
        "b"
    ],
    [
        "c",
        "d"
    ]
]

Encoding with Table Schema:

>>> result = df.to_json(orient="table")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "schema": {
        "fields": [
            {
                "name": "index",
                "type": "string"
            },
            {
                "name": "col 1",
                "type": "string"
            },
            {
                "name": "col 2",
                "type": "string"
            }
        ],
        "primaryKey": [
            "index"
        ],
        "pandas_version": "0.20.0"
    },
    "data": [
        {
            "index": "row 1",
            "col 1": "a",
            "col 2": "b"
        },
        {
            "index": "row 2",
            "col 1": "c",
            "col 2": "d"
        }
    ]
}
method

to_hdf(path_or_buf, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

.. warning::

One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.

For more information see the :ref:user guide <io.hdf5>.

Parameters
  • path_or_buf (str or pandas.HDFStore) File path or HDFStore object.
  • key (str) Identifier for the group in the store.
  • mode ({'a', 'w', 'r+'}, default 'a') Mode to open file:
    • 'w': write, a new file is created (an existing file with the same name would be deleted).
    • 'a': append, an existing file is opened for reading and writing, and if the file does not exist it is created.
    • 'r+': similar to 'a', but the file must already exist.
  • complevel ({0-9}, optional) Specifies a compression level for data.A value of 0 disables compression.
  • complib ({'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib') Specifies the compression library to be used.As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError.
  • append (bool, default False) For Table formats, append the input data to the existing.
  • format ({'fixed', 'table', None}, default 'fixed') Possible values:
    • 'fixed': Fixed format. Fast writing/reading. Not-appendable, nor searchable.
    • 'table': Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
    • If None, pd.get_option('io.hdf.default_format') is checked, followed by fallback to "fixed"
  • min_itemsize (dict or int, optional) Map column names to minimum string sizes for columns.
  • nan_rep (Any, optional) How to represent null values as str.Not allowed with append=True.
  • data_columns (list of columns or True, optional) List of columns to create as indexed data columns for on-diskqueries, or True to use all columns. By default only the axes of the object are indexed. See :ref:io.hdf5-query-data-columns. Applicable only to format='table'.
  • errors (str, default 'strict') Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:open for a full list of options.
See Also

read_hdf : Read from HDF file.DataFrame.to_parquet : Write a DataFrame to the binary parquet format. DataFrame.to_sql : Write to a SQL table. DataFrame.to_feather : Write out feather-format for DataFrames. DataFrame.to_csv : Write out to a csv file.

Examples
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},...                   index=['a', 'b', 'c'])
>>> df.to_hdf('data.h5', key='df', mode='w')

We can add another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf('data.h5', key='s')

Reading from HDF file:

>>> pd.read_hdf('data.h5', 'df')
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')
0    1
1    2
2    3
3    4
dtype: int64

Deleting file with data:

>>> import os
>>> os.remove('data.h5')
method

to_sql(name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.

Parameters
  • name (str) Name of SQL table.
  • con (sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection) Using SQLAlchemy makes it possible to use any DB supported by thatlibrary. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See here <https://docs.sqlalchemy.org/en/13/core/connections.html>_.
  • schema (str, optional) Specify the schema (if database flavor supports this). If None, usedefault schema.
  • if_exists ({'fail', 'replace', 'append'}, default 'fail') How to behave if the table already exists.
    • fail: Raise a ValueError.
    • replace: Drop the table before inserting new values.
    • append: Insert new values to the existing table.
  • index (bool, default True) Write DataFrame index as a column. Uses index_label as the columnname in the table.
  • index_label (str or sequence, default None) Column label for index column(s). If None is given (default) andindex is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
  • chunksize (int, optional) Specify the number of rows in each batch to be written at a time.By default, all rows will be written at once.
  • dtype (dict or scalar, optional) Specifying the datatype for columns. If a dictionary is used, thekeys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.
  • method ({None, 'multi', callable}, optional) Controls the SQL insertion clause used:
    • None : Uses standard SQL INSERT clause (one per row).
    • 'multi': Pass multiple values in a single INSERT clause.
    • callable with signature (pd_table, conn, keys, data_iter).
    Details and a sample callable implementation can be found in the section :ref:insert method <io.sql.method>.
Raises
  • ValueError When the table already exists and if_exists is 'fail' (thedefault).
See Also

read_sql : Read a DataFrame from a table.

Notes

Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

References

.. [1] https://docs.sqlalchemy.org.. [2] https://www.python.org/dev/peps/pep-0249/

Examples

Create an in-memory SQLite database.

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)

Create a table from scratch with 3 rows.

>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
     name
0  User 1
1  User 2
2  User 3
>>> df.to_sql('users', con=engine)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

An sqlalchemy.engine.Connection can also be passed to con:

>>> with engine.begin() as connection:
...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
...     df1.to_sql('users', con=connection, if_exists='append')

This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql('users', con=engine, if_exists='append')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
 (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
 (1, 'User 7')]

Overwrite the table with just df2.

>>> df2.to_sql('users', con=engine, if_exists='replace',
...            index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 6'), (1, 'User 7')]

Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
     A
0  1.0
1  NaN
2  2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql('integers', con=engine, index=False,
...           dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall()
[(1,), (None,), (2,)]
method

to_pickle(path, compression='infer', protocol=5, storage_options=None)

Pickle (serialize) object to file.

Parameters
  • path (str) File path where the pickled object will be stored.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') A string representing the compression to use in the output file. Bydefault, infers from the file extension in specified path. Compression mode may be any of the following possible values: {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and path_or_buf is path-like, then detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression). If dict given and mode is ‘zip’ or inferred as ‘zip’, other entries passed as additional compression options.
  • protocol (int) Int which indicates which protocol should be used by the pickler,default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
    .. [1] https://docs.python.org/3/library/pickle.html.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
See Also

read_pickle : Load pickled pandas object (or any object) from file.DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Examples
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> import os
>>> os.remove("./dummy.pkl")
method

to_clipboard(excel=True, sep=None, **kwargs)

Copy object to the system clipboard.

Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.

Parameters
  • excel (bool, default True) Produce output in a csv format for easy pasting into excel.
    • True, use the provided separator for csv pasting.
    • False, write a string representation of the object to the clipboard.
  • sep (str, default ``'\t'``) Field delimiter.
  • **kwargs These parameters will be passed to DataFrame.to_csv.
See Also

DataFrame.to_csv : Write a DataFrame to a comma-separated values (csv) file. read_clipboard : Read text from clipboard and pass to read_table.

Notes

Requirements for your platform.

  • Linux : xclip, or xsel (with PyQt4 modules)
  • Windows : none
  • OS X : none
Examples

Copy the contents of a DataFrame to the clipboard.

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',')  # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6

We can omit the index by passing the keyword index and setting it to false.

>>> df.to_clipboard(sep=',', index=False)  # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
method

to_xarray()

Return an xarray object from the pandas object.

Returns (xarray.DataArray or xarray.Dataset)

Data in the pandas structure converted to Dataset if the object isa DataFrame, or a DataArray if the object is a Series.

See Also

DataFrame.to_hdf : Write DataFrame to an HDF5 file.DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Notes

See the xarray docs <https://xarray.pydata.org/en/stable/>__

Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),...                    ('parrot', 'bird', 24.0, 2),
...                    ('lion', 'mammal', 80.5, 4),
...                    ('monkey', 'mammal', np.nan, 4)],
...                   columns=['name', 'class', 'max_speed',
...                            'num_legs'])
>>> df
     name   class  max_speed  num_legs
0  falcon    bird      389.0         2
1  parrot    bird       24.0         2
2    lion  mammal       80.5         4
3  monkey  mammal        NaN         4
>>> df.to_xarray()
<xarray.Dataset>
Dimensions:    (index: 4)
Coordinates:
  * index      (index) int64 0 1 2 3
Data variables:
    name       (index) object 'falcon' 'parrot' 'lion' 'monkey'
    class      (index) object 'bird' 'bird' 'mammal' 'mammal'
    max_speed  (index) float64 389.0 24.0 80.5 nan
    num_legs   (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. ,  24. ,  80.5,   nan])
Coordinates:
  * index    (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
...                         '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
...                               'animal': ['falcon', 'parrot',
...                                          'falcon', 'parrot'],
...                               'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
                   speed
date       animal
2018-01-01 falcon    350
           parrot     18
2018-01-02 falcon    361
           parrot     15
>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions:  (animal: 2, date: 2)
Coordinates:
  * date     (date) datetime64[ns] 2018-01-01 2018-01-02
  * animal   (animal) object 'falcon' 'parrot'
Data variables:
    speed    (date, animal) int64 350 18 361 15
method

to_latex(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, position=None)

Render object to a LaTeX tabular, longtable, or nested table/tabular.

Requires \usepackage{booktabs}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{table.tex}.

.. versionchanged:: 1.0.0 Added caption and label arguments.

.. versionchanged:: 1.2.0 Added position argument, changed meaning of caption argument.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (list of label, optional) The subset of columns to write. Writes all columns by default.
  • col_space (int, optional) The minimum width of each column.
  • header (bool or list of str, default True) Write out the column names. If a list of strings is given,it is assumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • na_rep (str, default 'NaN') Missing data representation.
  • formatters (list of functions or dict of {str: function}, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List must be of length equal to the number of columns.
  • float_format (one-parameter function or str, optional, default None) Formatter for floating point numbers. For examplefloat_format="%.2f" and float_format="{:0.2f}".format will both result in 0.1234 being formatted as 0.12.
  • sparsify (bool, optional) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row. By default, the value will be read from the config module.
  • index_names (bool, default True) Prints the names of the indexes.
  • bold_rows (bool, default False) Make the row labels bold in the output.
  • column_format (str, optional) The columns format as specified in LaTeX table format<https://en.wikibooks.org/wiki/LaTeX/Tables>__ e.g. 'rcl' for 3 columns. By default, 'l' will be used for all columns except columns of numbers, which default to 'r'.
  • longtable (bool, optional) By default, the value will be read from the pandas configmodule. Use a longtable environment instead of tabular. Requires adding a \usepackage{longtable} to your LaTeX preamble.
  • escape (bool, optional) By default, the value will be read from the pandas configmodule. When set to False prevents from escaping latex special characters in column names.
  • encoding (str, optional) A string representing the encoding to use in the output file,defaults to 'utf-8'.
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • multicolumn (bool, default True) Use \multicolumn to enhance MultiIndex columns.The default will be read from the config module.
  • multicolumn_format (str, default 'l') The alignment for multicolumns, similar to column_formatThe default will be read from the config module.
  • multirow (bool, default False) Use \multirow to enhance MultiIndex rows. Requires adding a\usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module.
  • caption (str or tuple, optional) Tuple (full_caption, short_caption),which results in \caption[short_caption]{full_caption}; if a single string is passed, no short caption will be set.
    .. versionadded:: 1.0.0
    .. versionchanged:: 1.2.0 Optionally allow caption to be a tuple (full_caption, short_caption).
  • label (str, optional) The LaTeX label to be placed inside \label{} in the output.This is used with \ref{} in the main .tex file.
    .. versionadded:: 1.0.0
  • position (str, optional) The LaTeX positional argument for tables, to be placed after\begin{} in the output.
    .. versionadded:: 1.2.0
    Returns
    -------
    str or None
        If buf is None, returns the result as a string. Otherwise returns
        None.
    
See Also

DataFrame.to_string : Render a DataFrame to a console-friendly tabular output. DataFrame.to_html : Render a DataFrame as an HTML table.

Examples
>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],...                   mask=['red', 'purple'],
...                   weapon=['sai', 'bo staff']))
>>> print(df.to_latex(index=False))  # doctest: +NORMALIZE_WHITESPACE
\begin{tabular}{lll}
 \toprule
       name &    mask &    weapon \\
 \midrule
    Raphael &     red &       sai \\
  Donatello &  purple &  bo staff \\
\bottomrule
\end{tabular}
method

to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)

Write object to a comma-separated values (csv) file.

Parameters
  • path_or_buf (str or file handle, default None) File path or object, if None is provided the result is returned asa string. If a non-binary file object is passed, it should be opened with newline='', disabling universal newlines. If a binary file object is passed, mode might need to contain a 'b'.
    .. versionchanged:: 1.2.0
    Support for binary file objects was introduced.
  • sep (str, default ',') String of length 1. Field delimiter for the output file.
  • na_rep (str, default '') Missing data representation.
  • float_format (str, default None) Format string for floating point numbers.
  • columns (sequence, optional) Columns to write.
  • header (bool or list of str, default True) Write out the column names. If a list of strings is given it isassumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • index_label (str or sequence, or False, default None) Column label for index column(s) if desired. If None is given, andheader and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.
  • mode (str) Python write mode, default 'w'.
  • encoding (str, optional) A string representing the encoding to use in the output file,defaults to 'utf-8'. encoding is not supported if path_or_buf is a non-binary file object.
  • compression (str or dict, default 'infer') If str, represents compression mode. If dict, value at 'method' isthe compression mode. Compression mode may be any of the following possible values: {'infer', 'gzip', 'bz2', 'zip', 'xz', None}. If compression mode is 'infer' and path_or_buf is path-like, then detect compression mode from the following extensions: '.gz', '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given and mode is one of {'zip', 'gzip', 'bz2'}, or inferred as one of the above, other entries passed as additional compression options.
    .. versionchanged:: 1.0.0
    May now be a dict with key 'method' as compression mode and other entries as additional compression options if compression mode is 'zip'.
    .. versionchanged:: 1.1.0
    Passing compression options as keys in dict is supported for compression modes 'gzip' and 'bz2' as well as 'zip'.
    .. versionchanged:: 1.2.0
    Compression is supported for binary file objects.
    
    .. versionchanged:: 1.2.0
    Previous versions forwarded dict entries for 'gzip' to
    `gzip.open` instead of `gzip.GzipFile` which prevented
    setting `mtime`.
    
  • quoting (optional constant from csv module) Defaults to csv.QUOTE_MINIMAL. If you have set a float_formatthen floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.
  • quotechar (str, default '\"') String of length 1. Character used to quote fields.
  • line_terminator (str, optional) The newline character or character sequence to use in the outputfile. Defaults to os.linesep, which depends on the OS in which this method is called ('\n' for linux, '\r\n' for Windows, i.e.).
  • chunksize (int or None) Rows to write at a time.
  • date_format (str, default None) Format string for datetime objects.
  • doublequote (bool, default True) Control quoting of quotechar inside a field.
  • escapechar (str, default None) String of length 1. Character used to escape sep and quotecharwhen appropriate.
  • decimal (str, default '.') Character recognized as decimal separator. E.g. use ',' forEuropean data.
  • errors (str, default 'strict') Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:open for a full list of options.
    .. versionadded:: 1.1.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
Returns (None or str)

If path_or_buf is None, returns the resulting csv format as astring. Otherwise returns None.

See Also

read_csv : Load a CSV file into a DataFrame.to_excel : Write DataFrame to an Excel file.

Examples
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'

Create 'out.zip' containing 'out.csv'

>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  # doctest: +SKIP
method

take(indices, axis=0, is_copy=None, **kwargs)

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

Parameters
  • indices (array-like) An array of ints indicating which positions to take.
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) The axis on which to select elements. 0 means that we areselecting rows, 1 means that we are selecting columns.
  • is_copy (bool) Before pandas 1.0, is_copy=False can be specified to ensurethat the return value is an actual copy. Starting with pandas 1.0, take always returns a copy, and the keyword is therefore deprecated.
    .. deprecated:: 1.0.0
  • **kwargs For compatibility with :meth:numpy.take. Has no effect on theoutput.
Returns (taken : same type as caller)

An array-like containing the elements taken from the object.

See Also

DataFrame.loc : Select a subset of a DataFrame by labels.DataFrame.iloc : Select a subset of a DataFrame by positions. numpy.take : Take elements from an array along an axis.

Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=['name', 'class', 'max_speed'],
...                   index=[0, 2, 3, 1])
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That's because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
     name   class  max_speed
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5
method

xs(key, axis=0, level=None, drop_level=True)

Return cross-section from the Series/DataFrame.

This method takes a key argument to select data at a particular level of a MultiIndex.

Parameters
  • key (label or tuple of label) Label contained in the index, or partially in a MultiIndex.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis to retrieve cross-section on.
  • level (object, defaults to first n levels (n=1 or len(key))) In case of a key partially contained in a MultiIndex, indicatewhich levels are used. Levels can be referred by label or position.
  • drop_level (bool, default True) If False, returns object with same levels as self.
Returns (Series or DataFrame)

Cross-section from the original Series or DataFramecorresponding to the selected index levels.

See Also

DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc : Purely integer-location based indexing for selection by position.

Notes

xs can not be used to set values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see :ref:MultiIndex Slicers <advanced.mi_slicers>.

Examples
>>> d = {'num_legs': [4, 4, 2, 2],...      'num_wings': [0, 0, 2, 2],
...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
...      'animal': ['cat', 'dog', 'bat', 'penguin'],
...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
                           num_legs  num_wings
class  animal  locomotion
mammal cat     walks              4          0
       dog     walks              4          0
       bat     flies              2          2
bird   penguin walks              2          2

Get values at specified index

>>> df.xs('mammal')
                   num_legs  num_wings
animal locomotion
cat    walks              4          0
dog    walks              4          0
bat    flies              2          2

Get values at several indexes

>>> df.xs(('mammal', 'dog'))
            num_legs  num_wings
locomotion
walks              4          0

Get values at specified index and level

>>> df.xs('cat', level=1)
                   num_legs  num_wings
class  locomotion
mammal walks              4          0

Get values at several indexes and levels

>>> df.xs(('bird', 'walks'),
...       level=[0, 'locomotion'])
         num_legs  num_wings
animal
penguin         2          2

Get values at specified column and axis

>>> df.xs('num_wings', axis=1)
class   animal   locomotion
mammal  cat      walks         0
        dog      walks         0
        bat      flies         2
bird    penguin  walks         2
Name: num_wings, dtype: int64
method

__delitem__(key)

Delete item

method

get(key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

method

reindex_like(other, method=None, copy=True, limit=None, tolerance=None)

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters
  • other (Object of the same data type) Its row and column indices are used to define the new indicesof this object.
  • method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) Method to use for filling holes in reindexed DataFrame.Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.
    • None (default): don't fill gaps
    • pad / ffill: propagate last valid observation forward to next valid
    • backfill / bfill: use next valid observation to fill gap
    • nearest: use nearest valid observations to fill gap.
  • copy (bool, default True) Return a new object, even if the passed indexes are the same.
  • limit (int, default None) Maximum number of consecutive labels to fill for inexact matches.
  • tolerance (optional) Maximum distance between original and new labels for inexactmatches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.
    Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type.
Returns (Series or DataFrame)

Same type as caller, but with changed indices on each axis.

See Also

DataFrame.set_index : Set row labels.DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...).

Examples
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],...                     [31, 87.8, 'high'],
...                     [22, 71.6, 'medium'],
...                     [35, 95, 'medium']],
...                    columns=['temp_celsius', 'temp_fahrenheit',
...                             'windspeed'],
...                    index=pd.date_range(start='2014-02-12',
...                                        end='2014-02-15', freq='D'))
>>> df1
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium
>>> df2 = pd.DataFrame([[28, 'low'],
...                     [30, 'low'],
...                     [35.1, 'medium']],
...                    columns=['temp_celsius', 'windspeed'],
...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
...                                            '2014-02-15']))
>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium
>>> df2.reindex_like(df1)
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       NaN
2014-02-15          35.1              NaN    medium
method

add_prefix(prefix)

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters
  • prefix (str) The string to add before each label.
Returns (Series or DataFrame)

New Series or DataFrame with updated labels.

See Also

Series.add_suffix: Suffix row labels with string suffix.DataFrame.add_suffix: Suffix column labels with string suffix.

Examples
>>> s = pd.Series([1, 2, 3, 4])>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_prefix('item_')
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_prefix('col_')
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6
method

add_suffix(suffix)

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters
  • suffix (str) The string to add after each label.
Returns (Series or DataFrame)

New Series or DataFrame with updated labels.

See Also

Series.add_prefix: Prefix row labels with string prefix.DataFrame.add_prefix: Prefix column labels with string prefix.

Examples
>>> s = pd.Series([1, 2, 3, 4])>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_suffix('_item')
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_suffix('_col')
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6
method

filter(items=None, like=None, regex=None, axis=None)

Subset the dataframe rows or columns according to the specified index labels.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Parameters
  • items (list-like) Keep labels from axis which are in items.
  • like (str) Keep labels from axis for which "like in label == True".
  • regex (str (regular expression)) Keep labels from axis for which re.search(regex, label) == True.
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) The axis to filter on, expressed either as an index (int)or axis name (str). By default this is the info axis, 'index' for Series, 'columns' for DataFrame.
See Also

DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array.

Notes

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

Examples
>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])
>>> df
        one  two  three
mouse     1    2      3
rabbit    4    5      6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
         one  three
mouse     1      3
rabbit    4      6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6
method

head(n=5)

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last n rows, equivalent to df[:-n].

Parameters
  • n (int, default 5) Number of rows to select.
Returns (same type as caller)

The first n rows of the caller object.

See Also

DataFrame.tail: Returns the last n rows.

Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the first 5 lines

>>> df.head()
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey

Viewing the first n lines (three in this case)

>>> df.head(3)
      animal
0  alligator
1        bee
2     falcon

For negative values of n

>>> df.head(-3)
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
method

tail(n=5)

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first n rows, equivalent to df[n:].

Parameters
  • n (int, default 5) Number of rows to select.
Returns (type of caller)

The last n rows of the caller object.

See Also

DataFrame.head : The first n rows of the caller object.

Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the last 5 lines

>>> df.tail()
   animal
4  monkey
5  parrot
6   shark
7   whale
8   zebra

Viewing the last n lines (three in this case)

>>> df.tail(3)
  animal
6  shark
7  whale
8  zebra

For negative values of n

>>> df.tail(-3)
   animal
3    lion
4  monkey
5  parrot
6   shark
7   whale
8   zebra
method

sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters
  • n (int, optional) Number of items from axis to return. Cannot be used with frac.Default = 1 if frac = None.
  • frac (float, optional) Fraction of axis items to return. Cannot be used with n.
  • replace (bool, default False) Allow or disallow sampling of the same row more than once.
  • weights (str or ndarray-like, optional) Default 'None' results in equal probability weighting.If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.
  • random_state (int, array-like, BitGenerator, np.random.RandomState, optional) If int, array-like, or BitGenerator (NumPy>=1.17), seed forrandom number generator If np.random.RandomState, use as numpy RandomState object.
    .. versionchanged:: 1.1.0
    array-like and BitGenerator (for NumPy>=1.17) object now passed to
    np.random.RandomState() as seed
    
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) Axis to sample. Accepts axis number or name. Default is stat axisfor given data type (0 for Series and DataFrames).
  • ignore_index (bool, default False) If True, the resulting index will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.3.0
Returns (Series or DataFrame)

A new object of same type as caller containing n items randomlysampled from the caller object.

See Also

DataFrameGroupBy.sample: Generates random samples from each group of a DataFrame object. SeriesGroupBy.sample: Generates random samples from each group of a Series object. numpy.random.choice: Generates a random sample from a given 1-D numpy array.

Notes

If frac > 1, replacement should be set to True.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],...                    'num_wings': [2, 0, 0, 0],
...                    'num_specimen_seen': [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df['num_legs'].sample(n=3, random_state=1)
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1)
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8
method

pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs).

Parameters
  • func (function) Function to apply to the Series/DataFrame.args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
  • args (iterable, optional) Positional arguments passed into func.
  • kwargs (mapping, optional) A dictionary of keyword arguments passed into func.
See Also

DataFrame.apply : Apply a function along input axis of DataFrame.DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Series.map : Apply a mapping correspondence on a :class:~pandas.Series.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing

>>> func(g(h(df), arg1=a), arg2=b, arg3=c)  # doctest: +SKIP

You can write

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe(func, arg2=b, arg3=c)
... )  # doctest: +SKIP

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose f takes its data as arg2:

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe((func, 'arg2'), arg1=a, arg3=c)
...  )  # doctest: +SKIP
method

__finalize__(other, method=None, **kwargs) → FrameOrSeries

Propagate metadata from other to self.

Parameters
  • other (the object from which to get the attributes that we are going) to propagate
  • method (str, optional) A passed method name providing context on where __finalize__was called.
    .. warning::
    The value passed as method are not currently considered stable across pandas releases.
method

__getattr__(name)

After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use.

method

__setattr__(name, value)

After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use.

method

astype(dtype, copy=True, errors='raise')

Cast a pandas object to a specified dtype dtype.

Parameters
  • dtype (data type, or dict of column name -> data type) Use a numpy.dtype or Python type to cast entire pandas object tothe same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types.
  • copy (bool, default True) Return a copy when copy=True (be very careful settingcopy=False as changes to values then may propagate to other pandas objects).
  • errors ({'raise', 'ignore'}, default 'raise') Control raising of exceptions on invalid data for provided dtype.
    • raise : allow exceptions to be raised
    • ignore : suppress exceptions. On error return original object.
See Also

to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type.

Notes

.. deprecated:: 1.3.0

Using ``astype`` to convert from timezone-naive dtype to
timezone-aware dtype is deprecated and will raise in a
future version.  Use :meth:`Series.dt.tz_localize` instead.
Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype('int32').dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({'col1': 'int32'}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype('int64')
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')
0    1
1    2
dtype: category
Categories (2, int64): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
...     categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Note that using copy=False and changing data on a new pandas object may propagate changes:

>>> s1 = pd.Series([1, 2])
>>> s2 = s1.astype('int64', copy=False)
>>> s2[0] = 10
>>> s1  # note that s1[0] has changed too
0    10
1     2
dtype: int64

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[ns]
method

__deepcopy__(memo=None) → FrameOrSeries

Parameters
  • memo, default None Standard signature. Unused
method

infer_objects()

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

See Also

to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to numeric type. convert_dtypes : Convert argument to best possible dtype.

Examples
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3
>>> df.dtypes
A    object
dtype: object
>>> df.infer_objects().dtypes
A    int64
dtype: object
method

convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True)

Convert columns to best possible dtypes using dtypes supporting pd.NA.

.. versionadded:: 1.0.0

Parameters
  • infer_objects (bool, default True) Whether object dtypes should be converted to the best possible types.
  • convert_string (bool, default True) Whether object dtypes should be converted to StringDtype().
  • convert_integer (bool, default True) Whether, if possible, conversion can be done to integer extension types.
  • convert_boolean (bool, defaults True) Whether object dtypes should be converted to BooleanDtypes().
  • convert_floating (bool, defaults True) Whether, if possible, conversion can be done to floating extension types.If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.
    .. versionadded:: 1.2.0
Returns (Series or DataFrame)

Copy of input object with new dtype.

See Also

infer_objects : Infer dtypes of objects.to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.

.. versionchanged:: 1.2 Starting with pandas 1.2, this method also converts float columns to the nullable floating extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples
>>> df = pd.DataFrame(...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0
>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10   <NA>
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0
>>> dfn.dtypes
a      Int32
b     string
c    boolean
d     string
e      Int64
f    Float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: object

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string
method

ffill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for :meth:DataFrame.fillna with method='ffill'.

Returns (Series/DataFrame or None)

Object with missing values filled or None if inplace=True.

method

bfill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for :meth:DataFrame.fillna with method='bfill'.

Returns (Series/DataFrame or None)

Object with missing values filled or None if inplace=True.

method

asof(where, subset=None)

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a :class:~pandas.DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters
  • where (date or array-like of dates) Date(s) before which the last row(s) are returned.
  • subset (str or array-like of str, default `None`) For DataFrame, if not None, only use these columns tocheck for NaNs.
Returns (scalar, Series, or DataFrame)

:

r , r n e

.

See Also

merge_asof : Perform an asof merge. Similar to left join.

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64
>>> s.asof(20)
2.0

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
2.0

Take all columns into consideration

>>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
...                    'b': [None, None, None, None, 500]},
...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
...                                           '2018-02-27 09:02:00',
...                                           '2018-02-27 09:03:00',
...                                           '2018-02-27 09:04:00',
...                                           '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']),
...         subset=['a'])
                         a   b
2018-02-27 09:03:30   30.0 NaN
2018-02-27 09:04:30   40.0 NaN
method

at_time(time, asof=False, axis=None)

Select values at particular time of day (e.g., 9:30AM).

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

between_time : Select values between particular times of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_at_time : Get just the index locations for values at particular time of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='12H')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-09 12:00:00  2
2018-04-10 00:00:00  3
2018-04-10 12:00:00  4
>>> ts.at_time('12:00')
                     A
2018-04-09 12:00:00  2
2018-04-10 12:00:00  4
method

between_time(start_time, end_time, include_start=True, include_end=True, axis=None)

Select values between particular times of the day (e.g., 9:00-9:30 AM).

By setting start_time to be later than end_time, you can get the times that are not between the two times.

Parameters
  • start_time (datetime.time or str) Initial time as a time filter limit.
  • end_time (datetime.time or str) End time as a time filter limit.
  • include_start (bool, default True) Whether the start time needs to be included in the result.
  • include_end (bool, default True) Whether the end time needs to be included in the result.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Determine range time on index or columns value.
Returns (Series or DataFrame)

Data from the original object filtered to the specified dates range.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

at_time : Select values at a particular time of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4
>>> ts.between_time('0:15', '0:45')
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are not between two times by setting start_time later than end_time:

>>> ts.between_time('0:45', '0:15')
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4
method

first(offset)

Select initial periods of time series data based on a date offset.

When having a DataFrame with dates as index, this function can select the first few rows based on a date offset.

Parameters
  • offset (str, DateOffset or dateutil.relativedelta) The offset length of the data that will be selected. For instance,'1M' will display all the rows having their index within the first month.
Returns (Series or DataFrame)

A subset of the caller.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

last : Select final periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the first 3 days:

>>> ts.first('3D')
            A
2018-04-09  1
2018-04-11  2

Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.

method

last(offset)

Select final periods of time series data based on a date offset.

For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.

Parameters
  • offset (str, DateOffset, dateutil.relativedelta) The offset length of the data that will be selected. For instance,'3D' will display all the rows having their index within the last 3 days.
Returns (Series or DataFrame)

A subset of the caller.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

first : Select initial periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the last 3 days:

>>> ts.last('3D')
            A
2018-04-13  3
2018-04-15  4

Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.

method

rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Index to direct ranking.
  • method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') How to rank the group of records that have the same value (i.e. ties):
    • average: average rank of the group
    • min: lowest rank in the group
    • max: highest rank in the group
    • first: ranks assigned in order they appear in the array
    • dense: like 'min', but rank always increases by 1 between groups.
  • numeric_only (bool, optional) For DataFrame objects, rank only numeric columns if set to True.
  • na_option ({'keep', 'top', 'bottom'}, default 'keep') How to rank NaN values:
    • keep: assign NaN rank to NaN values
    • top: assign lowest rank to NaN values
    • bottom: assign highest rank to NaN values
  • ascending (bool, default True) Whether or not the elements should be ranked in ascending order.
  • pct (bool, default False) Whether or not to display the returned rankings in percentileform.
Returns (same type as caller)

Return a Series or DataFrame with data ranks as values.

See Also

core.groupby.GroupBy.rank : Rank of values within each group.

Examples
>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

The following example shows how the method behaves with the above parameters:

  • default_rank: this is the default behaviour obtained without using any parameter.
  • max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since 'cat' and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
  • NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.
  • pct_rank: when setting pct = True, the ranking is expressed as percentile rank.
>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN
method

slice_shift(periods=1, axis=0)

Equivalent to shift without copying data.The shifted data will not include the dropped periods and the shifted axis will be smaller than the original.

.. deprecated:: 1.2.0 slice_shift is deprecated, use DataFrame/Series.shift instead.

Parameters
  • periods (int) Number of periods to move, can be positive or negative.

Notes

While the slice_shift is faster than shift, you may pay for it later during alignment.

method

tshift(periods=1, freq=None, axis=0)

Shift the time index, using the index's frequency if available.

.. deprecated:: 1.1.0 Use shift instead.

Parameters
  • periods (int) Number of periods to move, can be positive or negative.
  • freq (DateOffset, timedelta, or str, default None) Increment to use from the tseries moduleor time rule expressed as a string (e.g. 'EOM').
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default 0) Corresponds to the axis that contains the Index.

Notes

If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown

method

truncate(before=None, after=None, axis=None, copy=True)

Truncate a Series or DataFrame before and after some index value.

This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.

Parameters
  • before (date, str, int) Truncate all rows before this index value.
  • after (date, str, int) Truncate all rows after this index value.
  • axis ({0 or 'index', 1 or 'columns'}, optional) Axis to truncate. Truncates the index (rows) by default.
  • copy (bool, default is True,) Return a copy of the truncated section.
Returns (type of caller)

The truncated Series or DataFrame.

See Also

DataFrame.loc : Select a subset of a DataFrame by label.DataFrame.iloc : Select a subset of a DataFrame by position.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],...                    'B': ['f', 'g', 'h', 'i', 'j'],
...                    'C': ['k', 'l', 'm', 'n', 'o']},
...                   index=[1, 2, 3, 4, 5])
>>> df
   A  B  C
1  a  f  k
2  b  g  l
3  c  h  m
4  d  i  n
5  e  j  o
>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n

The columns of a DataFrame can be truncated.

>>> df.truncate(before="A", after="B", axis="columns")
   A  B
1  a  f
2  b  g
3  c  h
4  d  i
5  e  j

For Series, only rows can be truncated.

>>> df['A'].truncate(before=2, after=4)
2    b
3    c
4    d
Name: A, dtype: object

The index values in truncate can be datetimes or string dates.

>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
                     A
2016-01-31 23:59:56  1
2016-01-31 23:59:57  1
2016-01-31 23:59:58  1
2016-01-31 23:59:59  1
2016-02-01 00:00:00  1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
...             after=pd.Timestamp('2016-01-10')).tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

>>> df.truncate('2016-01-05', '2016-01-10').tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Note that truncate assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

>>> df.loc['2016-01-05':'2016-01-10', :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1
method

tz_convert(tz, axis=0, level=None, copy=True)

Convert tz-aware axis to target time zone.

Parameters
  • level (int, str, default None) If axis is a MultiIndex, convert a specific level. Otherwisemust be None.
  • copy (bool, default True) Also make a copy of the underlying data.
Returns ({klass})

Object with time zone converted axis.

Raises
  • TypeError If the axis is tz-naive.
method

tz_localize(tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise')

Localize tz-naive index of a Series or DataFrame to target time zone.

This operation localizes the Index. To localize the values in a timezone-naive Series, use :meth:Series.dt.tz_localize.

Parameters
  • level (int, str, default None) If axis ia a MultiIndex, localize a specific level. Otherwisemust be None.
  • copy (bool, default True) Also make a copy of the underlying data.
  • ambiguous ('infer', bool-ndarray, 'NaT', default 'raise') When clocks moved backward due to DST, ambiguous times may arise.For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.
    • 'infer' will attempt to infer fall dst-transition hours based on order
    • bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
    • 'NaT' will return NaT where there are ambiguous times
    • 'raise' will raise an AmbiguousTimeError if there are ambiguous times.
  • nonexistent (str, default 'raise') A nonexistent time does not exist in a particular timezonewhere clocks moved forward due to DST. Valid values are:
    • 'shift_forward' will shift the nonexistent time forward to the closest existing time
    • 'shift_backward' will shift the nonexistent time backward to the closest existing time
    • 'NaT' will return NaT where there are nonexistent times
    • timedelta objects will shift nonexistent times by the timedelta
    • 'raise' will raise an NonExistentTimeError if there are nonexistent times.
Returns (Series or DataFrame)

Same type as the input.

Raises
  • TypeError If the TimeSeries is tz-aware and tz is not None.
Examples

Localize local times:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00']))
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00    1
dtype: int64

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.Series(range(7),
...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 03:00:00',
...                                       '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00    0
2018-10-28 02:00:00+02:00    1
2018-10-28 02:30:00+02:00    2
2018-10-28 02:00:00+01:00    3
2018-10-28 02:30:00+01:00    4
2018-10-28 03:00:00+01:00    5
2018-10-28 03:30:00+01:00    6
dtype: int64

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.Series(range(3),
...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
...                                       '2018-10-28 02:36:00',
...                                       '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00    0
2018-10-28 02:36:00+02:00    1
2018-10-28 03:46:00+01:00    2
dtype: int64

If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or 'shift_forward' or 'shift_backward'.

>>> s = pd.Series(range(2),
...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
...                                       '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00    0
2015-03-29 03:30:00+02:00              1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))
2015-03-29 03:30:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
method

abs()

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns (abs)

Series/DataFrame containing the absolute value of each element.

See Also

numpy.absolute : Calculate the absolute value element-wise.

Notes

For complex inputs, 1.2 + 1j, the absolute value is :math:\sqrt{ a^2 + b^2 }.

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0   1 days
dtype: timedelta64[ns]

Select rows with data closest to certain value using argsort (from StackOverflow <https://stackoverflow.com/a/17758115>__).

>>> df = pd.DataFrame({
...     'a': [4, 5, 6, 7],
...     'b': [10, 20, 30, 40],
...     'c': [100, 50, -30, -50]
... })
>>> df
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50
method

describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters
  • percentiles (list-like of numbers, optional) The percentiles to include in the output. All shouldfall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
  • include ('all', list-like of dtypes or None (default), optional) A white list of data types to include in the result. Ignoredfor Series. Here are the options:
    • 'all' : All columns of the input will be included in the output.
    • A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
    • None (default) : The result will include all numeric columns.
  • exclude (list-like of dtypes or None (default), optional,) A black list of data types to omit from the result. Ignoredfor Series. Here are the options:
    • A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To exclude pandas categorical columns, use 'category'
    • None (default) : The result will exclude nothing.
  • datetime_is_numeric (bool, default False) Whether to treat datetime dtypes as numeric. This affects statisticscalculated for the column. For DataFrame input, this also controls whether datetime columns are included by default.
    .. versionadded:: 1.1.0
Returns (Series or DataFrame)

Summary statistics of the Series or Dataframe provided.

See Also

DataFrame.count: Count number of non-NA/null observations.DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype.

Notes

For numeric data, the result's index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result's index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value's frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01")
... ])
>>> s.describe(datetime_is_numeric=True)
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                   })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')  # doctest: +SKIP
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])  # doctest: +SKIP
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])
       categorical
count            3
unique           3
top              d
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])  # doctest: +SKIP
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])  # doctest: +SKIP
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0
method

pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)

Percentage change between the current and a prior element.

Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements.

Parameters
  • periods (int, default 1) Periods to shift for forming percent change.
  • fill_method (str, default 'pad') How to handle NAs before computing percent changes.
  • limit (int, default None) The number of consecutive NAs to fill before stopping.
  • freq (DateOffset, timedelta, or str, optional) Increment to use from time series API (e.g. 'M' or BDay()).
  • **kwargs Additional keyword arguments are passed intoDataFrame.shift or Series.shift.
Returns (chg : Series or DataFrame)

The same type as the calling object.

See Also

Series.diff : Compute the difference of two elements in a Series.DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64
>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13
>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351
>>> df.pct_change(axis='columns', periods=-1)
          2016      2015  2014
GOOG  0.179241  0.094112   NaN
APPL -0.252395 -0.011860   NaN
method

any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) Indicate which axis or axes should be reduced.
    • 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
    • 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
    • None : reduce all axes, return a scalar.
  • bool_only (bool, default None) Include only boolean columns. If None, will attempt to use everything,then use only boolean data. Not implemented for Series.
  • skipna (bool, default True) Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • **kwargs (any, default None) Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.

See Also

numpy.any : Numpy version of this method.Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
   A  B  C
0  1  0  0
1  2  2  0
>>> df.any()
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2
>>> df.any(axis='columns')
0    True
1    True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0
>>> df.any(axis='columns')
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)
method

all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) Indicate which axis or axes should be reduced.
    • 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
    • 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
    • None : reduce all axes, return a scalar.
  • bool_only (bool, default None) Include only boolean columns. If None, will attempt to use everything,then use only boolean data. Not implemented for Series.
  • skipna (bool, default True) Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • **kwargs (any, default None) Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.

See Also

Series.all : Return True if all elements are True.DataFrame.any : Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if column-wise values all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if row-wise values all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False
method

mad(axis=None, skipna=None, level=None)

Return the mean absolute deviation of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default None) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
method

sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

cummin(axis=None, skipna=True, *args, **kwargs)

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • *args, **kwargs Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

Return cumulative minimum of Series or DataFrame.

See Also

core.window.Expanding.min : Similar functionality but ignores NaN values. DataFrame.min : Return the minimum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
0    2.0
1    NaN
2    2.0
3   -1.0
4   -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0
method

cummax(axis=None, skipna=True, *args, **kwargs)

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • *args, **kwargs Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

Return cumulative maximum of Series or DataFrame.

See Also

core.window.Expanding.max : Similar functionality but ignores NaN values. DataFrame.max : Return the maximum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
0    2.0
1    NaN
2    5.0
3    5.0
4    5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0
method

cumsum(axis=None, skipna=True, *args, **kwargs)

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • *args, **kwargs Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

Return cumulative sum of Series or DataFrame.

See Also

core.window.Expanding.sum : Similar functionality but ignores NaN values. DataFrame.sum : Return the sum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
0    2.0
1    NaN
2    7.0
3    6.0
4    6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0
method

cumprod(axis=None, skipna=True, *args, **kwargs)

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • *args, **kwargs Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

Return cumulative product of Series or DataFrame.

See Also

core.window.Expanding.prod : Similar functionality but ignores NaN values. DataFrame.prod : Return the product over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
0     2.0
1     NaN
2    10.0
3   -10.0
4    -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0
method

sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • min_count (int, default 0) The required number of valid values to perform the operation. If fewer thanmin_count non-NA values are present the result will be NA.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.sum()
14

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
0.0

This can be controlled with the min_count parameter. For example, if you'd like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([], dtype="float64").sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nan
method

prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the product of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • min_count (int, default 0) The required number of valid values to perform the operation. If fewer thanmin_count non-NA values are present the result will be NA.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan
method

mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the mean of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the median of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.max()
8
method

min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.min()
0
method

rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, method='single')

Provide rolling window calculations.

Parameters
  • window (int, offset, or BaseIndexer subclass) Size of the moving window. This is the number of observations used forcalculating the statistic. Each window will be a fixed size.
    If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes.
    If a BaseIndexer subclass is passed, calculates the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, and closed will be passed to get_window_bounds.
  • min_periods (int, default None) Minimum number of observations in window required to have a value(otherwise result is NA). For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window.
  • center (bool, default False) Set the labels at the center of the window.
  • win_type (str, default None) Provide a window type. If None, all points are evenly weighted.See the notes below for further information.
  • on (str, optional) For a DataFrame, a datetime-like column or Index level on whichto calculate the rolling window, rather than the DataFrame's index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.
  • closed (str, default None) Make the interval closed on the 'right', 'left', 'both' or'neither' endpoints. Defaults to 'right'.
    .. versionchanged:: 1.2.0
    The closed parameter with fixed windows is now supported.
    
  • method (str {'single', 'table'}, default 'single') Execute the rolling operation per single column or row ('single')or over the entire object ('table').
    This argument is only implemented when specifying engine='numba' in the method call.
    .. versionadded:: 1.3.0
See Also

expanding : Provides expanding transformations.ewm : Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

To learn more about the offsets & frequency strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.

If win_type=None, all points are evenly weighted; otherwise, win_type can accept a string of any scipy.signal window function <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>__.

Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature. Please see the third example below on how to add the additional parameters.

Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

Rolling sum with a window length of 2, using the 'triang' window type.

>>> df.rolling(2, win_type='triang').sum()
     B
0  NaN
1  0.5
2  1.5
3  NaN
4  NaN

Rolling sum with a window length of 2, using the 'gaussian' window type (note how we need to specify std).

>>> df.rolling(2, win_type='gaussian').sum(std=3)
          B
0       NaN
1  0.986207
2  2.958621
3       NaN
4       NaN

Rolling sum with a window length of 2, min_periods defaults to the window length.

>>> df.rolling(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  NaN
4  NaN

Same as above, but explicitly set the min_periods

>>> df.rolling(2, min_periods=1).sum()
     B
0  0.0
1  1.0
2  3.0
3  2.0
4  4.0

Same as above, but with forward-looking windows

>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

A ragged (meaning not-a-regular frequency), time-indexed DataFrame

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
...                   index = [pd.Timestamp('20130101 09:00:00'),
...                            pd.Timestamp('20130101 09:00:02'),
...                            pd.Timestamp('20130101 09:00:03'),
...                            pd.Timestamp('20130101 09:00:05'),
...                            pd.Timestamp('20130101 09:00:06')])
>>> df
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. The default for min_periods is 1.

>>> df.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0
method

expanding(min_periods=1, center=None, axis=0, method='single')

Provide expanding transformations.

Parameters
  • min_periods (int, default 1) Minimum number of observations in window required to have a value(otherwise result is NA).
  • center (bool, default False) Set the labels at the center of the window.
  • method (str {'single', 'table'}, default 'single') Execute the rolling operation per single column or row ('single')or over the entire object ('table').
    This argument is only implemented when specifying engine='numba' in the method call.
    .. versionadded:: 1.3.0
See Also

rolling : Provides rolling window calculations.ewm : Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

Examples
>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.expanding(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  3.0
4  7.0
method

ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, times=None)

Provide exponential weighted (EW) functions.

Available EW functions: mean(), var(), std(), corr(), cov().

Exactly one parameter: com, span, halflife, or alpha must be provided.

Parameters
  • com (float, optional) Specify decay in terms of center of mass,:math:\alpha = 1 / (1 + com), for :math:com \geq 0.
  • span (float, optional) Specify decay in terms of span,:math:\alpha = 2 / (span + 1), for :math:span \geq 1.
  • halflife (float, str, timedelta, optional) Specify decay in terms of half-life,:math:\alpha = 1 - \exp\left(-\ln(2) / halflife\right), for :math:halflife > 0.
    If times is specified, the time unit (str or timedelta) over which an observation decays to half its value. Only applicable to mean() and halflife value will not apply to the other functions.
    .. versionadded:: 1.1.0
  • alpha (float, optional) Specify smoothing factor :math:\alpha directly,:math:0 < \alpha \leq 1.
  • min_periods (int, default 0) Minimum number of observations in window required to have a value(otherwise result is NA).
  • adjust (bool, default True) Divide by decaying adjustment factor in beginning periods to accountfor imbalance in relative weightings (viewing EWMA as a moving average).
    • When adjust=True (default), the EW function is calculated using weights :math:w_i = (1 - \alpha)^i. For example, the EW moving average of the series [:math:x_0, x_1, ..., x_t] would be:
    .. math:: y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
    • When adjust=False, the exponentially weighted function is calculated recursively:
    .. math:: \begin{split} y_0 &= x_0\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}
  • ignore_na (bool, default False) Ignore missing values when calculating weights; specify True to reproducepre-0.15.0 behavior.
    • When ignore_na=False (default), weights are based on absolute positions. For example, the weights of :math:x_0 and :math:x_2 used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:(1-\alpha)^2 and :math:1 if adjust=True, and :math:(1-\alpha)^2 and :math:\alpha if adjust=False.
    • When ignore_na=True (reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of :math:x_0 and :math:x_2 used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:1-\alpha and :math:1 if adjust=True, and :math:1-\alpha and :math:\alpha if adjust=False.
  • axis ({0, 1}, default 0) The axis to use. The value 0 identifies the rows, and 1identifies the columns.
  • times (str, np.ndarray, Series, default None) 0
    d .
    .
    .
    .
Returns (DataFrame)

A Window sub-classed for the particular operation.

See Also

rolling : Provides rolling window calculations.expanding : Provides expanding transformations.

Notes

: .

Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.ewm(com=0.5).mean()
          B
0  0.000000
1  0.750000
2  1.615385
3  1.615385
4  3.670213

Specifying times with a timedelta halflife when computing mean.

>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
          B
0  0.000000
1  0.585786
2  1.523889
3  1.523889
4  3.233686
method

first_valid_index()

Return index for first non-NA value or None, if no NA value is found.

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

method

last_valid_index()

Return index for last non-NA value or None, if no NA value is found.

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

method

__repr__() → str

Return a string representation for a particular DataFrame.

method

to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, min_rows=None, max_cols=None, show_dimensions=False, decimal='.', line_width=None, max_colwidth=None, encoding=None)

Render a DataFrame to a console-friendly tabular output.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (sequence, optional, default None) The subset of columns to write. Writes all columns by default.
  • col_space (int, list or dict of int, optional) The minimum width of each column.
  • header (bool or sequence, optional) Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
  • index (bool, optional, default True) Whether to print index (row) labels.
  • na_rep (str, optional, default 'NaN') String representation of NaN to use.
  • formatters (list, tuple or dict of one-param. functions, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
  • float_format (one-parameter function, optional, default None) Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
    .. versionchanged:: 1.2.0
  • sparsify (bool, optional, default True) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.
  • index_names (bool, optional, default True) Prints the names of the indexes.
  • justify (str, default None) How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
    • left
    • right
    • center
    • justify
    • justify-all
    • start
    • end
    • inherit
    • match-parent
    • initial
    • unset.
  • max_rows (int, optional) Maximum number of rows to display in the console.
  • min_rows (int, optional) The number of rows to display in the console in a truncated repr(when number of rows is above max_rows).
  • max_cols (int, optional) Maximum number of columns to display in the console.
  • show_dimensions (bool, default False) Display DataFrame dimensions (number of rows by number of columns).
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • line_width (int, optional) Width to wrap a line in characters.
  • max_colwidth (int, optional) Max width to truncate each column in characters. By default, no limit.
    .. versionadded:: 1.0.0
  • encoding (str, default "utf-8") Set character encoding.
    .. versionadded:: 1.0
Returns (str or None)

If buf is None, returns the result as a string. Otherwise returnsNone.

See Also

to_html : Convert DataFrame to HTML.

Examples
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}>>> df = pd.DataFrame(d)
>>> print(df.to_string())
   col1  col2
0     1     4
1     2     5
2     3     6
generator

items()

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields (label : object)

The column names for the DataFrame being iterated over.ent : Series The column entries belonging to each label, as a Series.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.

Examples
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
generator

iteritems()

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields (label : object)

The column names for the DataFrame being iterated over.ent : Series The column entries belonging to each label, as a Series.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.

Examples
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
generator

iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

Yields (index : label or tuple of label)

The index of the row. A tuple for a MultiIndex. : Series The data of the row as a Series.

See Also

DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.DataFrame.items : Iterate over (column name, Series) pairs.

Notes

  1. Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

To preserve dtypes while iterating over the rows, it is better to use :meth:itertuples which returns namedtuples of the values and which is generally faster than iterrows.

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

method

itertuples(index=True, name='Pandas')

Iterate over DataFrame rows as namedtuples.

Parameters
  • index (bool, default True) If True, return the index as the first element of the tuple.
  • name (str or None, default "Pandas") The name of the returned namedtuples or None to return regulartuples.
Returns (iterator)

An object to iterate over namedtuples for each row in theDataFrame with the first field possibly being the index and following fields being the column values.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.items : Iterate over (column name, Series) pairs.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. On python versions < 3.7 regular tuples are returned for DataFrames with a large number of columns (>254).

Examples
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},...                   index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):
...     print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name='Animal'):
...     print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
method

__len__() → int

Returns length of info axis, but here we use the index.

method

dot(other)

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using self @ other in Python >= 3.5.

Parameters
  • other (Series, DataFrame or array-like) The other object to compute the matrix product with.
Returns (Series or DataFrame)

If other is a Series, return the matrix product between self andother as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.

See Also

Series.dot: Similar method for Series.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0    -4
1     5
dtype: int64

Here we multiply a DataFrame with another DataFrame.

>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
    0   1
0   1   4
1   2   2

Note that the dot method give the same result as @

>>> df @ other
    0   1
0   1   4
1   2   2

The dot method works also if other is an np.array.

>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
    0   1
0   1   4
1   2   2

Note how shuffling of the objects does not change the result.

>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0    -4
1     5
dtype: int64
method

__matmul__(other) → DataFrame or Series

Matrix multiplication using binary @ operator in Python>=3.5.

method

__rmatmul__(other)

Matrix multiplication using binary @ operator in Python>=3.5.

classmethod

from_dict(data, orient='columns', dtype=None, columns=None)

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters
  • data (dict) Of the form {field : array-like} or {field : dict}.
  • orient ({'columns', 'index'}, default 'columns') The "orientation" of the data. If the keys of the passed dictshould be the columns of the resulting DataFrame, pass 'columns' (default). Otherwise if the keys should be rows, pass 'index'.
  • dtype (dtype, default None) Data type to force, otherwise infer.
  • columns (list, default None) Column labels to use when orient='index'. Raises a ValueErrorif used with orient='columns'.
See Also

DataFrame.from_records : DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame. DataFrame : DataFrame object creation using constructor.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
       0  1  2  3
row_1  3  2  1  0
row_2  a  b  c  d

When using the 'index' orientation, the column names can be specified manually:

>>> pd.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D'])
       A  B  C  D
row_1  3  2  1  0
row_2  a  b  c  d
method

to_numpy(dtype=None, copy=False, na_value=<no_default>)

Convert the DataFrame to a NumPy array.

By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are float16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.

Parameters
  • dtype (str or numpy.dtype, optional) The dtype to pass to :meth:numpy.asarray.
  • copy (bool, default False) Whether to ensure that the returned value is not a view onanother array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.
  • na_value (Any, optional) The value to use for missing values. The default value dependson dtype and the dtypes of the DataFrame columns.
    .. versionadded:: 1.1.0
See Also

Series.to_numpy : Similar method for Series.

Examples
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()array([[1, 3],
       [2, 4]])

With heterogeneous data, the lowest common type will have to be used.

>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
       [2. , 4.5]])

For a mix of numeric and non-numeric types, the output array will have object dtype.

>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
       [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
method

to_dict(orient='dict', into=<class 'dict'>)

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Parameters
  • orient (str {'dict', 'list', 'series', 'split', 'records', 'index'}) Determines the type of the values of the dictionary.
    • 'dict' (default) : dict like {column -> {index -> value}}
    • 'list' : dict like {column -> [values]}
    • 'series' : dict like {column -> Series(values)}
    • 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
    • 'records' : list like [{column -> value}, ... , {column -> value}]
    • 'index' : dict like {index -> {column -> value}}
    Abbreviations are allowed. s indicates series and sp indicates split.
  • into (class, default dict) The collections.abc.Mapping subclass used for all Mappingsin the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.
Returns (dict, list or collections.abc.Mapping)

Return a collections.abc.Mapping object representing the DataFrame.The resulting transformation depends on the orient parameter.

See Also

DataFrame.from_dict: Create a DataFrame from a dictionary.DataFrame.to_json: Convert a DataFrame to JSON format.

Examples
>>> df = pd.DataFrame({'col1': [1, 2],...                    'col2': [0.5, 0.75]},
...                   index=['row1', 'row2'])
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
method

to_gbq(destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=False, table_schema=None, location=None, progress_bar=True, credentials=None)

Write a DataFrame to a Google BigQuery table.

This function requires the pandas-gbq package <https://pandas-gbq.readthedocs.io>__.

See the How to authenticate with Google BigQuery <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>__ guide for authentication instructions.

Parameters
  • destination_table (str) Name of table to be written, in the form dataset.tablename.
  • project_id (str, optional) Google BigQuery Account project ID. Optional when available fromthe environment.
  • chunksize (int, optional) Number of rows to be inserted in each chunk from the dataframe.Set to None to load the whole dataframe at once.
  • reauth (bool, default False) Force Google BigQuery to re-authenticate the user. This is usefulif multiple accounts are used.
  • if_exists (str, default 'fail') Behavior when the destination table exists. Value can be one of:
    'fail' If table exists raise pandasgbq.gbq.TableCreationError. 'replace' If table exists, drop it, recreate it, and insert data. 'append' If table exists, insert data. Create if does not exist.
  • auth_local_webserver (bool, default False) Use the local webserver flow instead of the console flowwhen getting user credentials.
    .. _local webserver flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server .. _console flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
    New in version 0.2.0 of pandas-gbq.
  • table_schema (list of dicts, optional) List of BigQuery table fields to which according DataFramecolumns conform to, e.g. [{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field.
    New in version 0.3.1 of pandas-gbq.
  • location (str, optional) Location where the load job should run. See the BigQuery locationsdocumentation <https://cloud.google.com/bigquery/docs/dataset-locations>__ for a list of available locations. The location must match that of the target dataset.
    New in version 0.5.0 of pandas-gbq.
  • progress_bar (bool, default True) Use the library tqdm to show the progress bar for the upload,chunk by chunk.
    New in version 0.5.0 of pandas-gbq.
  • credentials (google.auth.credentials.Credentials, optional) Credentials for accessing Google APIs. Use this parameter tooverride default credentials, such as to use Compute Engine :class:google.auth.compute_engine.Credentials or Service Account :class:google.oauth2.service_account.Credentials directly.
    New in version 0.8.0 of pandas-gbq.
See Also

pandas_gbq.to_gbq : This function in the pandas-gbq library.read_gbq : Read a DataFrame from Google BigQuery.

classmethod

from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)

Convert structured or record ndarray to DataFrame.

Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame.

Parameters
  • data (structured ndarray, sequence of tuples or dicts, or DataFrame) Structured input data.
  • index (str, list of fields, array-like) Field of array to use as the index, alternately a specific set ofinput labels to use.
  • exclude (sequence, default None) Columns or fields to exclude.
  • columns (sequence, default None) Column names to use. If the passed data do not have namesassociated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).
  • coerce_float (bool, default False) Attempt to convert values of non-string, non-numeric objects (likedecimal.Decimal) to floating point, useful for SQL result sets.
  • nrows (int, default None) Number of rows to read if data is an iterator.
See Also

DataFrame.from_dict : DataFrame from dict of array-like or dicts.DataFrame : DataFrame object creation using constructor.

Examples

Data can be provided as a structured ndarray:

>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of dicts:

>>> data = [{'col_1': 3, 'col_2': 'a'},
...         {'col_1': 2, 'col_2': 'b'},
...         {'col_1': 1, 'col_2': 'c'},
...         {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of tuples with corresponding columns:

>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d
method

to_records(index=True, column_dtypes=None, index_dtypes=None)

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters
  • index (bool, default True) Include index in resulting record array, stored in 'index'field or using the index label, if set.
  • column_dtypes (str, type, dict, default None) If a string or type, the data type to store all columns. Ifa dictionary, a mapping of column names and indices (zero-indexed) to specific data types.
  • index_dtypes (str, type, dict, default None) If a string or type, the data type to store all index levels. Ifa dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.
    This mapping is applied only if index=True.
Returns (numpy.recarray)

NumPy ndarray with the DataFrame labels as fields and each rowof the DataFrame as entries.

See Also

DataFrame.from_records: Convert structured or record ndarray to DataFrame. numpy.recarray: An ndarray that allows field access using attributes, analogous to typed columns in a spreadsheet.

Examples
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},...                   index=['a', 'b'])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to 'index'. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
method

to_stata(path, convert_dates=None, write_index=True, byteorder=None, time_stamp=None, data_label=None, variable_labels=None, version=114, convert_strl=None, compression='infer', storage_options=None)

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. "dta" files contain a Stata dataset.

Parameters
  • path (str, buffer or path object) String, path object (pathlib.Path or py._path.local.LocalPath) orobject implementing a binary write() function. If using a buffer then the buffer will not be automatically closed after the file data has been written.
    .. versionchanged:: 1.0.0
    Previously this was "fname"
  • convert_dates (dict) Dictionary mapping columns containing datetime types to statainternal format to use when writing the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to 'tc'. Raises NotImplementedError if a datetime column has timezone information.
  • write_index (bool) Write the index to Stata dataset.
  • byteorder (str) Can be ">", "<", "little", or "big". default is sys.byteorder.
  • time_stamp (datetime) A datetime to use as file creation date. Default is the currenttime.
  • data_label (str, optional) A label for the data set. Must be 80 characters or smaller.
  • variable_labels (dict) Dictionary containing columns as keys and variable labels asvalues. Each label must be 80 characters or smaller.
  • version ({{114, 117, 118, 119, None}}, default 114) Version to use in the output dta file. Set to None to let pandasdecide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.
    Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.
    .. versionchanged:: 1.0.0
    Added support for formats 118 and 119.
    
  • convert_strl (list, optional) List of column names to convert to string columns to Stata StrLformat. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.
  • compression (str or dict, default 'infer') For on-the-fly compression of the output dta. If string, specifiescompression mode. If dict, value at key 'method' specifies compression mode. Compression mode must be one of {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}. If compression mode is 'infer' and fname is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no compression). If dict and compression mode is one of {{'zip', 'gzip', 'bz2'}}, or inferred as one of the above, other entries passed as additional compression options.
    .. versionadded:: 1.1.0
  • {storage_options} 0
Raises
  • NotImplementedError
    • If datetimes contain timezone information
    • Column dtype is not representable in Stata
  • ValueError
    • Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime
    • Column listed in convert_dates is not in DataFrame
    • Categorical label contains more than 32,000 characters
See Also

read_stata : Import Stata data files.io.stata.StataWriter : Low-level writer for Stata data files. io.stata.StataWriter117 : Low-level writer for version 117 files.

Examples
>>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',...                               'parrot'],
...                    'speed': [350, 18, 361, 15]}})
>>> df.to_stata('animals.dta')  # doctest: +SKIP
method

to_feather(path, **kwargs)

Write a DataFrame to the binary Feather format.

Parameters
  • path (str or file-like object) If a string, it will be used as Root Directory path.
  • **kwargs Additional keywords passed to :func:pyarrow.feather.write_feather.Starting with pyarrow 0.17, this includes the compression, compression_level, chunksize and version keywords.
    .. versionadded:: 1.1.0
method

to_markdown(buf=None, mode='wt', index=True, storage_options=None, **kwargs)

Print DataFrame in Markdown-friendly format.

.. versionadded:: 1.0.0

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • mode (str, optional) Mode in which file is opened, "wt" by default.
  • index (bool, optional, default True) Add index (row) labels.
    .. versionadded:: 1.1.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
  • **kwargs These parameters will be passed to tabulate <https://pypi.org/project/tabulate>_.
Returns (str)

DataFrame in Markdown-friendly format.

Notes

Requires the tabulate <https://pypi.org/project/tabulate>_ package.

Examples
>>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")>>> print(s.to_markdown())
|    | animal   |
|---:|:---------|
|  0 | elk      |
|  1 | pig      |
|  2 | dog      |
|  3 | quetzal  |

Output markdown with a tabulate option.

>>> print(s.to_markdown(tablefmt="grid"))
+----+----------+
|    | animal   |
+====+==========+
|  0 | elk      |
+----+----------+
|  1 | pig      |
+----+----------+
|  2 | dog      |
+----+----------+
|  3 | quetzal  |
+----+----------+
method

to_parquet(path=None, engine='auto', compression='snappy', index=None, partition_cols=None, storage_options=None, **kwargs)

Write a DataFrame to the binary parquet format.

This function writes the dataframe as a parquet file <https://parquet.apache.org/>_. You can choose different parquet backends, and have the option of compression. See :ref:the user guide <io.parquet> for more details.

Parameters
  • path (str or file-like object, default None) If a string, it will be used as Root Directory pathwhen writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function) or io.BytesIO. The engine fastparquet does not accept file-like objects. If path is None, a bytes object is returned.
    .. versionchanged:: 1.2.0
    Previously this was "fname"
  • engine ({{'auto', 'pyarrow', 'fastparquet'}}, default 'auto') Parquet library to use. If 'auto', then the optionio.parquet.engine is used. The default io.parquet.engine behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable.
  • compression ({{'snappy', 'gzip', 'brotli', None}}, default 'snappy') Name of the compression to use. Use None for no compression.
  • index (bool, default None) If True, include the dataframe's index(es) in the file output.If False, they will not be written to the file. If None, similar to True the dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output.
  • partition_cols (list, optional, default None) Column names by which to partition the dataset.Columns are partitioned in the order they are given. Must be None if path is not a string.
  • **kwargs Additional arguments passed to the parquet library. See:ref:pandas io <io.parquet> for more details.
  • {storage_options} 0
See Also

read_parquet : Read a parquet file.DataFrame.to_csv : Write a csv file. DataFrame.to_sql : Write to a sql table. DataFrame.to_hdf : Write to hdf.

Notes

This function requires either the fastparquet <https://pypi.org/project/fastparquet> or pyarrow <https://arrow.apache.org/docs/python/> library.

Examples
>>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})>>> df.to_parquet('df.parquet.gzip',
...               compression='gzip')  # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip')  # doctest: +SKIP
   col1  col2
0     1     3
1     2     4

If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don't use partition_cols, which creates multiple files.

>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()
method

to_html(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', bold_rows=True, classes=None, escape=True, notebook=False, border=None, table_id=None, render_links=False, encoding=None)

Render a DataFrame as an HTML table.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (sequence, optional, default None) The subset of columns to write. Writes all columns by default.
  • col_space (str or int, list or dict of int or str, optional) The minimum width of each column in CSS length units. An int is assumed to be px units.
    .. versionadded:: 0.25.0 Ability to use str.
  • header (bool, optional) Whether to print column labels, default True.
  • index (bool, optional, default True) Whether to print index (row) labels.
  • na_rep (str, optional, default 'NaN') String representation of NaN to use.
  • formatters (list, tuple or dict of one-param. functions, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
  • float_format (one-parameter function, optional, default None) Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
    .. versionchanged:: 1.2.0
  • sparsify (bool, optional, default True) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.
  • index_names (bool, optional, default True) Prints the names of the indexes.
  • justify (str, default None) How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
    • left
    • right
    • center
    • justify
    • justify-all
    • start
    • end
    • inherit
    • match-parent
    • initial
    • unset.
  • max_rows (int, optional) Maximum number of rows to display in the console.
  • max_cols (int, optional) Maximum number of columns to display in the console.
  • show_dimensions (bool, default False) Display DataFrame dimensions (number of rows by number of columns).
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • bold_rows (bool, default True) Make the row labels bold in the output.
  • classes (str or list or tuple, default None) CSS class(es) to apply to the resulting html table.
  • escape (bool, default True) Convert the characters <, >, and & to HTML-safe sequences.
  • notebook ({True, False}, default False) Whether the generated HTML is for IPython Notebook.
  • border (int) A border=border attribute is included in the opening<table> tag. Default pd.options.display.html.border.
  • table_id (str, optional) A css id is included in the opening <table> tag if specified.
  • render_links (bool, default False) Convert URLs to HTML links.
  • encoding (str, default "utf-8") Set character encoding.
    .. versionadded:: 1.0
  • min_rows (int, optional) The number of rows to display in the console in a truncated repr(when number of rows is above max_rows).
Returns (str or None)

If buf is None, returns the result as a string. Otherwise returnsNone.

See Also

to_string : Convert DataFrame to a string.

method

to_xml(path_or_buffer=None, index=True, root_name='data', row_name='row', na_rep=None, attr_cols=None, elem_cols=None, namespaces=None, prefix=None, encoding='utf-8', xml_declaration=True, pretty_print=True, parser='lxml', stylesheet=None, compression='infer', storage_options=None)

Render a DataFrame to an XML document.

.. versionadded:: 1.3.0

Parameters
  • path_or_buffer (str, path object or file-like object, optional) File to write output to. If None, the output is returned as astring.
  • index (bool, default True) Whether to include index in XML document.
  • root_name (str, default 'data') The name of root element in XML document.
  • row_name (str, default 'row') The name of row element in XML document.
  • na_rep (str, optional) Missing data representation.
  • attr_cols (list-like, optional) List of columns to write as attributes in row element.Hierarchical columns will be flattened with underscore delimiting the different levels.
  • elem_cols (list-like, optional) List of columns to write as children in row element. By default,all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels.
  • namespaces (dict, optional) All namespaces to be defined in root element. Keys of dictshould be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example, ::
    namespaces = {"": "https://example.com"}
    
  • prefix (str, optional) Namespace prefix to be used for every element and/or attributein document. This should be one of the keys in namespaces dict.
  • encoding (str, default 'utf-8') Encoding of the resulting document.
  • xml_declaration (bool, default True) Whether to include the XML declaration at start of document.
  • pretty_print (bool, default True) Whether output should be pretty printed with indentation andline breaks.
  • parser ({'lxml','etree'}, default 'lxml') Parser module to use for building of tree. Only 'lxml' and'etree' are supported. With 'lxml', the ability to use XSLT stylesheet is supported.
  • stylesheet (str, path object or file-like object, optional) A URL, file-like object, or a raw string containing an XSLTscript used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requires lxml to be installed. Only XSLT 1.0 scripts and not later versions is currently supported.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') For on-the-fly decompression of on-disk data. If 'infer', then usegzip, bz2, zip or xz if path_or_buffer is a string ending in '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
Returns (None or str)

If io is None, returns the resulting XML format as astring. Otherwise returns None.

See Also

to_json : Convert the pandas object to a JSON string.to_html : Convert DataFrame to a html.

Examples
>>> df = pd.DataFrame({'shape': ['square', 'circle', 'triangle'],...                    'degrees': [360, 360, 180],
...                    'sides': [4, np.nan, 3]})
>>> df.to_xml()  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row>
    <index>0</index>
    <shape>square</shape>
    <degrees>360</degrees>
    <sides>4.0</sides>
  </row>
  <row>
    <index>1</index>
    <shape>circle</shape>
    <degrees>360</degrees>
    <sides/>
  </row>
  <row>
    <index>2</index>
    <shape>triangle</shape>
    <degrees>180</degrees>
    <sides>3.0</sides>
  </row>
</data>
>>> df.to_xml(attr_cols=[
...           'index', 'shape', 'degrees', 'sides'
...           ])  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row index="0" shape="square" degrees="360" sides="4.0"/>
  <row index="1" shape="circle" degrees="360"/>
  <row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>
>>> df.to_xml(namespaces={"doc": "https://example.com"},
...           prefix="doc")  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
  <doc:row>
    <doc:index>0</doc:index>
    <doc:shape>square</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides>4.0</doc:sides>
  </doc:row>
  <doc:row>
    <doc:index>1</doc:index>
    <doc:shape>circle</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides/>
  </doc:row>
  <doc:row>
    <doc:index>2</doc:index>
    <doc:shape>triangle</doc:shape>
    <doc:degrees>180</doc:degrees>
    <doc:sides>3.0</doc:sides>
  </doc:row>
</doc:data>
method

info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None, null_counts=None)

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.

Parameters
  • verbose (bool, optional) Whether to print the full summary. By default, the setting inpandas.options.display.max_info_columns is followed.
  • buf (writable buffer, defaults to sys.stdout) Where to send the output. By default, the output is printed tosys.stdout. Pass a writable buffer if you need to further process the output.
  • max_cols (int, optional) When to switch from the verbose to the truncated output. If theDataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.
  • memory_usage (bool, str, optional) Specifies whether total memory usage of the DataFrameelements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting.
    True always show memory usage. False never shows memory usage. A value of 'deep' is equivalent to "True with deep introspection". Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources.
  • show_counts (bool, optional) Whether to show the non-null counts. By default, this is shownonly if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.
  • null_counts (bool, optional) .. deprecated:: 1.2.0 Use show_counts instead.
  • data (DataFrame) DataFrame to print information about.
Returns (None)

This method prints a summary of a DataFrame and returns None.

See Also

DataFrame.describe: Generate descriptive statistics of DataFrame columns. DataFrame.memory_usage: Memory usage of DataFrame columns.

Examples
>>> int_values = [1, 2, 3, 4, 5]>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
...                   "float_col": float_values})
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  # doctest: +SKIP
...     f.write(s)
260

The memory_usage parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 165.9 MB
method

memory_usage(index=True, deep=False)

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters
  • index (bool, default True) Specifies whether to include the memory usage of the DataFrame'sindex in returned Series. If index=True, the memory usage of the index is the first item in the output.
  • deep (bool, default False) If True, introspect the data deeply by interrogatingobject dtypes for system-level memory consumption, and include it in the returned values.
Returns (Series)

A Series whose index is the original column names and whose valuesis the memory usage of each column in bytes.

See Also

numpy.ndarray.nbytes : Total bytes consumed by the elements of an ndarray. Series.memory_usage : Bytes consumed by a Series. Categorical : Memory-efficient array for string values with many repeated values. DataFrame.info : Concise summary of a DataFrame.

Examples
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
...              for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
   int64  float64            complex128  object  bool
0      1      1.0              1.0+0.0j       1  True
1      1      1.0              1.0+0.0j       1  True
2      1      1.0              1.0+0.0j       1  True
3      1      1.0              1.0+0.0j       1  True
4      1      1.0              1.0+0.0j       1  True
>>> df.memory_usage()
Index           128
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64
>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)
Index            128
int64          40000
float64        40000
complex128     80000
object        180000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['object'].astype('category').memory_usage(deep=True)
5244
method

transpose(*args, copy=False)

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property :attr:.T is an accessor to the method :meth:transpose.

Parameters
  • *args (tuple, optional) Accepted for compatibility with NumPy.
  • copy (bool, default False) Whether to copy the data after transposing, even for DataFrameswith a single dtype.
    Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.
Returns (DataFrame)

The transposed DataFrame.

See Also

numpy.transpose : Permute the dimensions of a given array.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    int64
col2    int64
dtype: object
>>> df1_transposed.dtypes
0    int64
1    int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {'name': ['Alice', 'Bob'],
...       'score': [9.5, 8],
...       'employed': [False, True],
...       'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
    name  score  employed  kids
0  Alice    9.5     False     0
1    Bob    8.0      True     0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
              0     1
name      Alice   Bob
score       9.5   8.0
employed  False  True
kids          0     0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name         object
score       float64
employed       bool
kids          int64
dtype: object
>>> df2_transposed.dtypes
0    object
1    object
dtype: object
method

query(expr, inplace=False, **kwargs)

Query the columns of a DataFrame with a boolean expression.

Parameters
  • expr (str) The query string to evaluate.
    You can refer to variables in the environment by prefixing them with an '@' character like @a + b.
    You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named "Area (cm^2)" would be referenced as Area (cm^2)). Column names which are Python keywords (like "list", "for", "import", etc) cannot be used.
    For example, if one of your columns is called a a and you want to sum it with b, your query should be `a a` + b.
    .. versionadded:: 0.25.0 Backtick quoting introduced.
    .. versionadded:: 1.0.0 Expanding functionality of backtick quoting for more than only spaces.
  • inplace (bool) Whether the query should modify the data in place or returna modified copy.
  • **kwargs See the documentation for :func:eval for complete detailson the keyword arguments accepted by :meth:DataFrame.query.
Returns (DataFrame or None)

DataFrame resulting from the provided query expression orNone if inplace=True.

See Also

eval : Evaluate a string describing operations on DataFrame columns. DataFrame.eval : Evaluate a string describing operations on DataFrame columns.

Notes

The result of the evaluation of this expression is first passed to :attr:DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to :meth:DataFrame.__getitem__.

This method uses the top-level :func:eval function to evaluate the passed query.

The :meth:~pandas.DataFrame.query method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, :keyword:and and :keyword:or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The :attr:DataFrame.index and :attr:DataFrame.columns attributes of the :class:~pandas.DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.

For further details and examples see the query documentation in :ref:indexing <indexing.query>.

Backtick quoted variables

Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.

During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped).

In a special case, quotes that make a pair around a backtick can confuse the parser. For example, it's` > `that's will raise an error, as it forms a quoted string ('s > `that') with a backtick inside.

See also the Python documentation about lexical analysis (https://docs.python.org/3/reference/lexical_analysis.html) in combination with the source code in :mod:pandas.core.computation.parsing.

Examples
>>> df = pd.DataFrame({'A': range(1, 6),...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
   A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.query('A > B')
   A  B  C C
4  5  2    6

The previous expression is equivalent to

>>> df[df.A > df.B]
   A  B  C C
4  5  2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
   A   B  C C
0  1  10   10

The previous expression is equivalent to

>>> df[df.B == df['C C']]
   A   B  C C
0  1  10   10
method

eval(expr, inplace=False, **kwargs)

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters
  • expr (str) The expression string to evaluate.
  • inplace (bool, default False) If the expression contains an assignment, whether to perform theoperation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
  • **kwargs See the documentation for :func:eval for complete detailson the keyword arguments accepted by :meth:~pandas.DataFrame.query.
Returns (ndarray, scalar, pandas object, or None)

The result of the evaluation or None if inplace=True.

See Also

DataFrame.query : Evaluates a boolean expression to query the columns of a frame. DataFrame.assign : Can evaluate an expression or function to create new values for a column. eval : Evaluate a Python expression as a string using various backends.

Notes

For more details see the API documentation for :func:~eval. For detailed examples see :ref:enhancing performance with eval <enhancingperf.eval>.

Examples
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2

Use inplace=True to modify the original DataFrame.

>>> df.eval('C = A + B', inplace=True)
>>> df
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3
method

select_dtypes(include=None, exclude=None)

Return a subset of the DataFrame's columns based on the column dtypes.

Parameters
  • include, exclude : scalar or list-like A selection of dtypes or strings to be included/excluded. At leastone of these parameters must be supplied.
Returns (DataFrame)

The subset of the frame including the dtypes in include andexcluding the dtypes in exclude.

Raises
  • ValueError
    • If both of include and exclude are empty
    • If include and exclude have overlapping elements
    • If any kind of string dtype is passed in.
See Also

DataFrame.dtypes: Return Series with the data type of each column.

Notes

  • To select all numeric types, use np.number or 'number'
  • To select strings you must use the object dtype, but note that this will return all object dtype columns
  • See the numpy dtype hierarchy <https://numpy.org/doc/stable/reference/arrays.scalars.html>__
  • To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
  • To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'
  • To select Pandas categorical dtypes, use 'category'
  • To select Pandas datetimetz dtypes, use 'datetimetz' (new in 0.20.0) or 'datetime64[ns, tz]'
Examples
>>> df = pd.DataFrame({'a': [1, 2] * 3,...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0
>>> df.select_dtypes(include='bool')
   b
0  True
1  False
2  True
3  False
4  True
5  False
>>> df.select_dtypes(include=['float64'])
   c
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0
>>> df.select_dtypes(exclude=['int64'])
       b    c
0   True  1.0
1  False  2.0
2   True  1.0
3  False  2.0
4   True  1.0
5  False  2.0
method

insert(loc, column, value, allow_duplicates=False)

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

Parameters
  • loc (int) Insertion index. Must verify 0 <= loc <= len(columns).
  • column (str, number, or hashable object) Label of the inserted column.
See Also

Index.insert : Insert new item by index.

Examples
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})>>> df
   col1  col2
0     1     3
1     2     4
>>> df.insert(1, "newcol", [99, 99])
>>> df
   col1  newcol  col2
0     1      99     3
1     2      99     4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
   col1  col1  newcol  col2
0   100     1      99     3
1   100     2      99     4

Notice that pandas uses index alignment in case of value from type Series:

>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
   col0  col1  col1  newcol  col2
0   NaN   100     1      99     3
1   5.0   100     2      99     4
method

assign(**kwargs)

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters
  • **kwargs (dict of {str: callable or Series}) The column names are keywords. If the values arecallable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn't check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
Returns (DataFrame)

A new DataFrame with the new columns in addition toall the existing columns.

Notes

Assigning multiple columns within the same assign is possible. Later items in '**kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order.

Examples
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15
method

lookup(row_labels, col_labels)

Label-based "fancy indexing" function for DataFrame.Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair.

.. deprecated:: 1.2.0 DataFrame.lookup is deprecated, use DataFrame.melt and DataFrame.loc instead. For further details see :ref:Looking up values by index/column labels <indexing.lookup>.

Parameters
  • row_labels (sequence) The row labels to use for lookup.
  • col_labels (sequence) The column labels to use for lookup.
Returns (numpy.ndarray)

The found values.

method

align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters
  • axis (allowed axis of the other object, default None) Align on index (0), columns (1), or both (None).
  • level (int or level name, default None) Broadcast across a level, matching Index values on thepassed MultiIndex level.
  • copy (bool, default True) Always returns new objects. If copy=False and no reindexing isrequired then original objects are returned.
  • fill_value (scalar, default np.NaN) Value to use for missing values. Defaults to NaN, but can be any"compatible" value.
  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) Method to use for filling holes in reindexed Series:
    • pad / ffill: propagate last valid observation forward to next valid.
    • backfill / bfill: use NEXT valid observation to fill gap.
  • limit (int, default None) If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
  • fill_axis ({0 or 'index', 1 or 'columns'}, default 0) Filling axis, method and limit.
  • broadcast_axis ({0 or 'index', 1 or 'columns'}, default None) Broadcast values along this axis, if aligning two objects ofdifferent dimensions.
Returns (left, right) : (DataFrame, type of other)

Aligned objects.

method

set_axis(labels, axis=0, inplace=False)

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or Index.

Parameters
  • labels (list-like, Index) The values for the new index.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to update. The value 0 identifies the rows, and 1 identifies the columns.
  • inplace (bool, default False) Whether to return a new DataFrame instance.
Returns (renamed : DataFrame or None)

An object of type DataFrame or None if inplace=True.

See Also

DataFrame.renameaxis : Alter the name of the index or columns.

    Examples
    --------

~~~python

df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) ~~~

    Change the row labels.
>>> df.set_axis(['a', 'b', 'c'], axis='index')
   A  B
a  1  4
b  2  5
c  3  6
    Change the column labels.
>>> df.set_axis(['I', 'II'], axis='columns')
   I  II
0  1   4
1  2   5
2  3   6
    Now, update the labels inplace.
>>> df.set_axis(['i', 'ii'], axis='columns', inplace=True)
>>> df
   i  ii
0  1   4
1  2   5
2  3   6
method

drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide <advanced.shown_levels> for more information about the now unused levels.

Parameters
  • labels (single label or list-like) Index or column labels to drop.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Whether to drop labels from the index (0 or 'index') orcolumns (1 or 'columns').
  • index (single label or list-like) Alternative to specifying axis (labels, axis=0is equivalent to index=labels).
  • columns (single label or list-like) Alternative to specifying axis (labels, axis=1is equivalent to columns=labels).
  • level (int or level name, optional) For MultiIndex, level from which the labels will be removed.
  • inplace (bool, default False) If False, return a copy. Otherwise, do operationinplace and return None.
  • errors ({'ignore', 'raise'}, default 'raise') If 'ignore', suppress error and only existing labels aredropped.
Returns (DataFrame or None)

DataFrame without the removed index or column labels orNone if inplace=True.

Raises
  • KeyError If any of the labels is not found in the selected axis.
See Also

DataFrame.loc : Label-location based indexer for selection by label.DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. DataFrame.dropduplicates : Return DataFrame with duplicate rows removed, optionally only considering certain columns. Series.drop : Return Series with specified index labels removed.

Examples
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2
>>> df.drop(index='cow', columns='small')
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3
>>> df.drop(index='length', level=1)
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8
method

rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore')

Alter axes labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error.

See the :ref:user guide <basics.rename> for more.

Parameters
  • mapper (dict-like or function) Dict-like or function transformations to apply tothat axis' values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.
  • index (dict-like or function) Alternative to specifying axis (mapper, axis=0is equivalent to index=mapper).
  • columns (dict-like or function) Alternative to specifying axis (mapper, axis=1is equivalent to columns=mapper).
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis to target with mapper. Can be either the axis name('index', 'columns') or number (0, 1). The default is 'index'.
  • copy (bool, default True) Also copy underlying data.
  • inplace (bool, default False) Whether to return a new DataFrame. If True then value of copy isignored.
  • level (int or level name, default None) In case of a MultiIndex, only rename labels in the specifiedlevel.
  • errors ({'ignore', 'raise'}, default 'ignore') If 'raise', raise a KeyError when a dict-like mapper, index,or columns contains labels that are not present in the Index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored.
Returns (DataFrame or None)

DataFrame with the renamed axis labels or None if inplace=True.

Raises
  • KeyError If any of the labels is not found in the selected axis and"errors='raise'".
See Also

DataFrame.renameaxis : Set the name of the axis.

Examples

DataFrame.rename supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)
  • (mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters:

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6
>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6
method

fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)

Fill NA/NaN values using the specified method.

Parameters
  • value (scalar, dict, Series, or DataFrame) Value to use to fill holes (e.g. 0), alternately adict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.
  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) Method to use for filling holes in reindexed Seriespad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap.
  • axis ({0 or 'index', 1 or 'columns'}) Axis along which to fill missing values.
  • inplace (bool, default False) If True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in a DataFrame).
  • limit (int, default None) If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
  • downcast (dict, default is None) A dict of item->dtype of what to downcast if possible,or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).
Returns (DataFrame or None)

Object with missing values filled or None if inplace=True.

See Also

interpolate : Fill NaN values using interpolation.reindex : Conform object to new index. asfreq : Convert TimeSeries to specified frequency.

Examples
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, 5],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

Replace all NaN elements with 0s.

>>> df.fillna(0)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4

We can also propagate non-null values forward or backward.

>>> df.fillna(method="ffill")
    A   B   C   D
0   NaN 2.0 NaN 0
1   3.0 4.0 NaN 1
2   3.0 4.0 NaN 5
3   3.0 3.0 NaN 4

Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 2.0 1
2   0.0 1.0 2.0 5
3   0.0 3.0 2.0 4

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 NaN 1
2   NaN 1.0 NaN 5
3   NaN 3.0 NaN 4

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4
method

pop(item)

Return item and drop from frame. Raise KeyError if not found.

Parameters
  • item (label) Label of column to be popped.
Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN
>>> df.pop('class')
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object
>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN
method

replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')

Replace values given in to_replace with value.

Values of the DataFrame are replaced with other values dynamically.

This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Parameters
  • to_replace (str, regex, list, dict, Series, int, float, or None) How to find the values that will be replaced.
    • numeric, str or regex:
      • numeric: numeric values equal to to_replace will be replaced with value
      • str: string exactly matching to_replace will be replaced with value
      • regex: regexs matching to_replace will be replaced with value
    • list of str, regex, or numeric:
      • First, if to_replace and value are both lists, they must be the same length.
      • Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for value since there are only a few possible substitution regexes you can use.
      • str, regex and numeric rules apply as above.
    • dict:
      • Dicts can be used to specify different replacement values for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value 'a' with 'b' and 'y' with 'z'. To use a dict in this way the value parameter should be None.
      • For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column 'a' and the value 'z' in column 'b' and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.
      • For a DataFrame nested dictionaries, e.g., {'a': {'b': np.nan}}, are read as follows: look in column 'a' for the value 'b' and replace it with NaN. The value parameter should be None to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
    • None:
      • This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.
    See the examples section for examples of each of these.
  • value (scalar, dict, list, str, regex, default None) Value to replace any values matching to_replace with.For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.
  • inplace (bool, default False) If True, performs operation inplace and returns None.
  • limit (int, default None) Maximum size gap to forward or backward fill.
  • regex (bool or same types as `to_replace`, default False) Whether to interpret to_replace and/or value as regularexpressions. If this is True then to_replace must be a string. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.
  • method ({'pad', 'ffill', 'bfill', `None`}) The method to use when for replacement, when to_replace is ascalar, list or tuple and value is None.
    .. versionchanged:: 0.23.0 Added to DataFrame.
Returns (DataFrame)

Object after replacement.

Raises
  • AssertionError
    • If regex is not a bool and to_replace is not None.
  • TypeError
    • If to_replace is not a scalar, array-like, dict, or None
    • If to_replace is a dict and value is not a list, dict, ndarray, or Series
    • If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series.
    • When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced
  • ValueError
    • If a list or an ndarray is passed to to_replace and value but they are not the same length.
See Also

DataFrame.fillna : Fill NA values.DataFrame.where : Replace values based on boolean condition. Series.str.replace : Simple string replacement.

Notes

  • Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
  • Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
  • This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.
  • When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.
Examples

*

) ) 5 1 2 3 4 4

, , ) ) C a b c d e

*

) C a b c d e

) C a b c d e

) 0 3 3 3 4 4

*

) C a b c d e

) C a b c d e

) C a b c d e

*

, ) ) B c w z

) B c r z

) B c w z

) B c w z

) B c w z

d s :

)

e . o :

) 0 e e b e t

r e 0 . o :

) 0 0 0 b b t

method

shift(periods=1, freq=None, axis=0, fill_value=<no_default>)

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as "infer" as long as either freq or inferred_freq attribute is set in the index.

Parameters
  • periods (int) Number of periods to shift. Can be positive or negative.
  • freq (DateOffset, tseries.offsets, timedelta, or str, optional) Offset to use from the tseries module or time rule (e.g. 'EOM').If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as "infer" then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
  • axis ({0 or 'index', 1 or 'columns', None}, default None) Shift direction.
  • fill_value (object, optional) The scalar value to use for newly introduced missing values.the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. :attr:NaT is used. For extension dtypes, self.dtype.na_value is used.
    .. versionchanged:: 1.1.0
Returns (DataFrame)

Copy of input object, shifted.

See Also

Index.shift : Shift values of Index.DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex. tshift : Shift the time index, using the index's frequency if available.

Examples
>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],...                    "Col2": [13, 23, 18, 33, 48],
...                    "Col3": [17, 27, 22, 37, 52]},
...                   index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52
>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0
>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01   NaN    10    13
2020-01-02   NaN    20    23
2020-01-03   NaN    15    18
2020-01-04   NaN    30    33
2020-01-05   NaN    45    48
>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27
>>> df.shift(periods=3, freq="D")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
>>> df.shift(periods=3, freq="infer")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
method

set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.

Parameters
  • keys (label or array-like or list of labels/arrays) This parameter can be either a single column key, a single array ofthe same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, "array" encompasses :class:Series, :class:Index, np.ndarray, and instances of :class:~collections.abc.Iterator.
  • drop (bool, default True) Delete columns to be used as the new index.
  • append (bool, default False) Whether to append columns to existing index.
  • inplace (bool, default False) If True, modifies the DataFrame in place (do not create a new object).
  • verify_integrity (bool, default False) Check the new index for duplicates. Otherwise defer the check untilnecessary. Setting to False will improve the performance of this method.
Returns (DataFrame or None)

Changed row labels or None if inplace=True.

See Also

DataFrame.reset_index : Opposite of set_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.

Examples
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],...                    'year': [2012, 2014, 2013, 2014],
...                    'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31

Set the index to become the 'month' column:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31

Create a MultiIndex using columns 'year' and 'month':

>>> df.set_index(['year', 'month'])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a MultiIndex using an Index and a column:

>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
      month  year  sale
1 1       1  2012    55
2 4       4  2014    40
3 9       7  2013    84
4 16     10  2014    31
method

reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')

Reset the index, or a level of it.

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters
  • level (int, str, tuple, or list, default None) Only remove the given levels from the index. Removes all levels bydefault.
  • drop (bool, default False) Do not try to insert index into dataframe columns. This resetsthe index to the default integer index.
  • inplace (bool, default False) Modify the DataFrame in place (do not create a new object).
  • col_level (int or str, default 0) If the columns have multiple levels, determines which level thelabels are inserted into. By default it is inserted into the first level.
  • col_fill (object, default '') If the columns have multiple levels, determines how the otherlevels are named. If None then the index name is repeated.
Returns (DataFrame or None)

DataFrame with the new index or None if inplace=True.

See Also

DataFrame.set_index : Opposite of reset_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.

Examples
>>> df = pd.DataFrame([('bird', 389.0),...                    ('bird', 24.0),
...                    ('mammal', 80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
...                    ( 24.0, 'fly'),
...                    ( 80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump
method

isna()

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.

See Also

DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
method

isnull()

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.

See Also

DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
method

notna()

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or :attr:numpy.NaN, get mapped to False values.

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.

See Also

DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
method

notnull()

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or :attr:numpy.NaN, get mapped to False values.

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.

See Also

DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
method

dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Remove missing values.

See the :ref:User Guide <missing_data> for more on which values are considered missing, and how to work with missing data.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Determine if rows or columns which contain missing values areremoved.
    • 0, or 'index' : Drop rows which contain missing values.
    • 1, or 'columns' : Drop columns which contain missing value.
    .. versionchanged:: 1.0.0
    Pass tuple or list to drop on multiple axes. Only a single axis is allowed.
  • how ({'any', 'all'}, default 'any') Determine if row or column is removed from DataFrame, when we haveat least one NA or all NA.
    • 'any' : If any NA values are present, drop that row or column.
    • 'all' : If all values are NA, drop that row or column.
  • thresh (int, optional) Require that many non-NA values.
  • subset (array-like, optional) Labels along other axis to consider, e.g. if you are dropping rowsthese would be a list of columns to include.
  • inplace (bool, default False) If True, do operation inplace and return None.
Returns (DataFrame or None)

DataFrame with NA entries dropped from it or None if inplace=True.

See Also

DataFrame.isna: Indicate missing values.DataFrame.notna : Indicate existing (non-missing) values. DataFrame.fillna : Replace missing values. Series.dropna : Drop missing values. Index.dropna : Drop missing indices.

Examples
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep the DataFrame with valid entries in the same variable.

>>> df.dropna(inplace=True)
>>> df
     name        toy       born
1  Batman  Batmobile 1940-04-25
method

drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False)

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters
  • subset (column label or sequence of labels, optional) Only consider certain columns for identifying duplicates, bydefault use all of the columns.
  • keep ({'first', 'last', False}, default 'first') Determines which duplicates (if any) to keep.
    • - first : Drop duplicates except for the first occurrence.
    • - last : Drop duplicates except for the last occurrence.
    • - False : Drop all duplicates.
  • inplace (bool, default False) Whether to drop duplicates in place or to return a copy.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
Returns (DataFrame or None)

DataFrame with duplicates removed or None if inplace=True.

See Also

DataFrame.value_counts: Count unique combinations of columns.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0
method

duplicated(subset=None, keep='first')

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters
  • subset (column label or sequence of labels, optional) Only consider certain columns for identifying duplicates, bydefault use all of the columns.
  • keep ({'first', 'last', False}, default 'first') Determines which duplicates (if any) to mark.
    • first : Mark duplicates as True except for the first occurrence.
    • last : Mark duplicates as True except for the last occurrence.
    • False : Mark all duplicates as True.
Returns (Series)

Boolean series for each duplicated rows.

See Also

Index.duplicated : Equivalent method on index.Series.duplicated : Equivalent method on Series. Series.dropduplicates : Remove duplicate values from Series. DataFrame.dropduplicates : Remove duplicate values from DataFrame.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, for each set of duplicated values, the first occurrence is set on False and all others on True.

>>> df.duplicated()
0    False
1     True
2    False
3    False
4    False
dtype: bool

By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True.

>>> df.duplicated(keep='last')
0     True
1    False
2    False
3    False
4    False
dtype: bool

By setting keep on False, all duplicates are True.

>>> df.duplicated(keep=False)
0     True
1     True
2    False
3    False
4    False
dtype: bool

To find duplicates on specific column(s), use subset.

>>> df.duplicated(subset=['brand'])
0    False
1     True
2    False
3     True
4     True
dtype: bool
method

sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

Sort by the values along either axis.

Parameters
  • by (str or list of str) Name or list of names to sort by.
    • if axis is 0 or 'index' then by may contain index levels and/or column labels.
    • if axis is 1 or 'columns' then by may contain column levels and/or index labels.
  • ers. If this is a list of bools, must match the length of by.
  • tin (meth:`sorted` function, with the notable difference that) key function should be vectorized. It should expect a
Returns (DataFrame or None)

DataFrame with sorted values or None if inplace=True.

See Also

DataFrame.sort_index : Sort a DataFrame by the index.Series.sort_values : Similar method for a Series.

Examples
>>> df = pd.DataFrame({...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Sort by col1

>>> df.sort_values(by=['col1'])
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
  col1  col2  col3 col4
1    A     1     1    B
0    A     2     0    a
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
  col1  col2  col3 col4
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B
3  NaN     8     4    D

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
  col1  col2  col3 col4
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B

Sorting with a key function

>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
   col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Natural sort with the key argument, using the natsort <https://github.com/SethMMorton/natsort> package.

>>> df = pd.DataFrame({
...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
...    "value": [10, 20, 30, 40, 50]
... })
>>> df
    time  value
0    0hr     10
1  128hr     20
2   72hr     30
3   48hr     40
4   96hr     50
>>> from natsort import index_natsorted
>>> df.sort_values(
...    by="time",
...    key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
    time  value
0    0hr     10
3   48hr     40
2   72hr     30
4   96hr     50
1  128hr     20
method

sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis along which to sort. The value 0 identifies the rows,and 1 identifies the columns.
  • level (int or level name or list of ints or list of level names) If not None, sort on values in specified index level(s).
  • ascending (bool or list-like of bools, default True) Sort ascending vs. descending. When the index is a MultiIndex thesort direction can be controlled for each level individually.
  • inplace (bool, default False) If True, perform operation in-place.
  • kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') Choice of sorting algorithm. See also :func:numpy.sort for moreinformation. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
  • na_position ({'first', 'last'}, default 'last') Puts NaNs at the beginning if first; last puts NaNs at the end.Not implemented for MultiIndex.
  • sort_remaining (bool, default True) If True and sorting by level and index is multilevel, sort by otherlevels too (in order) after sorting by specified level.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
  • key (callable, optional) If not None, apply the key function to the index valuesbefore sorting. This is similar to the key argument in the builtin :meth:sorted function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.
    .. versionadded:: 1.1.0
Returns (DataFrame or None)

The original DataFrame sorted by the labels or None if inplace=True.

See Also

Series.sort_index : Sort Series by the index.DataFrame.sort_values : Sort DataFrame by the value. Series.sort_values : Sort Series by the value.

Examples
>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],...                   columns=['A'])
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

>>> df.sort_index(ascending=False)
     A
234  3
150  5
100  1
29   2
1    4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4
method

value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)

Return a Series containing counts of unique rows in the DataFrame.

.. versionadded:: 1.1.0

Parameters
  • subset (list-like, optional) Columns to use when counting unique combinations.
  • normalize (bool, default False) Return proportions rather than frequencies.
  • sort (bool, default True) Sort by frequencies.
  • ascending (bool, default False) Sort in ascending order.
  • dropna (bool, default True) Don’t include counts of rows that contain NA values.
    .. versionadded:: 1.3.0
See Also

Series.value_counts: Equivalent method on Series.

Notes

The returned Series will have a MultiIndex with one level per input column. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],...                    'num_wings': [2, 0, 0, 0]},
...                   index=['falcon', 'dog', 'cat', 'ant'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0
>>> df.value_counts()
num_legs  num_wings
4         0            2
2         2            1
6         0            1
dtype: int64
>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
dtype: int64
>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
dtype: int64
>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
2         2            0.25
6         0            0.25
dtype: float64

With dropna set to False we can also count rows with NA values.

>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
>>> df
  first_name middle_name
0       John       Smith
1       Anne        <NA>
2       John        <NA>
3       Beth      Louise
>>> df.value_counts()
first_name  middle_name
Beth        Louise         1
John        Smith          1
dtype: int64
>>> df.value_counts(dropna=False)
first_name  middle_name
Anne        NaN            1
Beth        Louise         1
John        Smith          1
            NaN            1
dtype: int64
method

nlargest(n, columns, keep='first')

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters
  • n (int) Number of rows to return.
  • columns (label or list of labels) Column label(s) to order by.
  • keep ({'first', 'last', 'all'}, default 'first') Where there are duplicate values:
    • first : prioritize the first occurrence(s)
    • last : prioritize the last occurrence(s)
    • all : do not drop any duplicates, even it means selecting more than n items.
Returns (DataFrame)

The first n rows ordered by the given columns in descendingorder.

See Also

DataFrame.nsmallest : Return the first n rows ordered by columns in ascending order. DataFrame.sort_values : Sort DataFrame by the values. DataFrame.head : Return the first n rows without re-ordering.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,...                                   434000, 434000, 337000, 11300,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column "population".

>>> df.nlargest(3, 'population')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'population', keep='last')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', all duplicate items are maintained:

>>> df.nlargest(3, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column "population" and then "GDP", we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['population', 'GDP'])
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN
method

nsmallest(n, columns, keep='first')

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters
  • n (int) Number of items to retrieve.
  • columns (list or str) Column name or names to order by.
  • keep ({'first', 'last', 'all'}, default 'first') Where there are duplicate values:
    • first : take the first occurrence.
    • last : take the last occurrence.
    • all : do not drop any duplicates, even it means selecting more than n items.
See Also

DataFrame.nlargest : Return the first n rows ordered by columns in descending order. DataFrame.sort_values : Sort DataFrame by the values. DataFrame.head : Return the first n rows without re-ordering.

Examples
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,...                                   434000, 434000, 337000, 337000,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column "population".

>>> df.nsmallest(3, 'population')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, 'population', keep='last')
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', all duplicate items are maintained:

>>> df.nsmallest(3, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column "population" and then "GDP", we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ['population', 'GDP'])
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR
method

swaplevel(i=-2, j=-1, axis=0)

Swap levels i and j in a :class:MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to swap levels on. 0 or 'index' for row-wise, 1 or'columns' for column-wise.
  • i, j : int or str Levels of the indices to be swapped. Can pass level name as string.
Returns (DataFrame)

DataFrame with levels swapped in MultiIndex.

Examples
>>> df = pd.DataFrame(...     {"Grade": ["A", "B", "A", "C"]},
...     index=[
...         ["Final exam", "Final exam", "Coursework", "Coursework"],
...         ["History", "Geography", "History", "Geography"],
...         ["January", "February", "March", "April"],
...     ],
... )
>>> df
                                    Grade
Final exam  History     January      A
            Geography   February     B
Coursework  History     March        A
            Geography   April        C

In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.

>>> df.swaplevel()
                                    Grade
Final exam  January     History         A
            February    Geography       B
Coursework  March       History         A
            April       Geography       C

By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.

>>> df.swaplevel(0)
                                    Grade
January     History     Final exam      A
February    Geography   Final exam      B
March       History     Coursework      A
April       Geography   Coursework      C

We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.

>>> df.swaplevel(0, 1)
                                    Grade
History     Final exam  January         A
Geography   Final exam  February        B
History     Coursework  March           A
Geography   Coursework  April           C
method

reorder_levels(order, axis=0)

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters
  • order (list of int or list of str) List representing new level order. Reference level by number(position) or by key (label).
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Where to reorder levels.
method

compare(other, align_axis=1, keep_shape=False, keep_equal=False)

Compare to another DataFrame and show the differences.

.. versionadded:: 1.1.0

Parameters
  • other (DataFrame) Object to compare with.
  • align_axis ({0 or 'index', 1 or 'columns'}, default 1) Determine which axis to align the comparison on.
    • 0, or 'index' : Resulting differences are stacked vertically with rows drawn alternately from self and other.
    • 1, or 'columns' : Resulting differences are aligned horizontally with columns drawn alternately from self and other.
  • keep_shape (bool, default False) If true, all rows and columns are kept.Otherwise, only the ones with different values are kept.
  • keep_equal (bool, default False) If true, the result keeps values that are equal.Otherwise, equal values are shown as NaNs.
Returns (DataFrame)

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with 'self' and 'other' stacked alternately at the inner level.

Raises
  • ValueError When the two DataFrames don't have identical labels or shape.
See Also

Series.compare : Compare with another Series and show differences.DataFrame.equals : Test whether two objects contain the same elements.

Notes

Matching NaNs will not appear as a difference.

Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames

Examples
>>> df = pd.DataFrame(...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0
>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Align the differences on columns

>>> df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0
method

combine(other, func, fill_value=None, overwrite=True)

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters
  • other (DataFrame) The DataFrame to merge column-wise.
  • func (function) Function that takes two series as inputs and return a Series or ascalar. Used to merge the two dataframes column by columns.
  • fill_value (scalar value, default None) The value to fill NaNs with prior to passing any column to themerge func.
  • overwrite (bool, default True) If True, columns in self that do not exist in other will beoverwritten with NaNs.
Returns (DataFrame)

Combination of the provided DataFrames.

See Also

DataFrame.combinefirst : Combine two DataFrame objects and default to non-null values in frame calling the method.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
   A    B
0  0 -5.0
1  0  4.0

However, if the same element in both dataframes is None, that None is preserved

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
    A    B
0  0 -5.0
1  0  3.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
   A    B   C
0  0.0  NaN NaN
1  0.0  3.0 NaN
2  NaN  3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
     A    B   C
0  0.0  NaN NaN
1  0.0  3.0 1.0
2  NaN  3.0 1.0
method

combine_first(other)

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters
  • other (DataFrame) Provided DataFrame to use to fill null values.
Returns (DataFrame)

The result of combining the provided DataFrame with the other object.

See Also

DataFrame.combine : Perform series-wise operation on two DataFrames using a given function.

Examples
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0
method

update(other, join='left', overwrite=True, filter_func=None, errors='ignore')

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters
  • other (DataFrame, or object coercible into a DataFrame) Should have at least one matching index/column labelwith the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.
  • join ({'left'}, default 'left') Only left join is implemented, keeping the index and columns of theoriginal object.
  • overwrite (bool, default True) How to handle non-NA values for overlapping keys:
    • True: overwrite original DataFrame's values with values from other.
    • False: only update values that are NA in the original DataFrame.
  • filter_func (callable(1d-array) -> bool 1d-array, optional) Can choose to replace values other than NA. Return True for valuesthat should be updated.
  • errors ({'raise', 'ignore'}, default 'ignore') If 'raise', will raise a ValueError if the DataFrame and otherboth contain non-NA data in the same place.
Raises
  • NotImplementedError
    • If join != 'left'
  • ValueError
    • When errors='raise' and there's overlapping non-NA data.
    • When errors is not either 'ignore' or 'raise'
See Also

dict.update : Similar method for dictionaries.DataFrame.merge : For column(s)-on-column(s) operations.

Examples
>>> df = pd.DataFrame({'A': [1, 2, 3],...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame's length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

For Series, its name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  y
2  c  e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  x
1  b  d
2  c  e

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0
method

groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=<no_default>, observed=False, dropna=True)

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters
  • by (mapping, function, label, or list of labels) Used to determine the groups for the groupby.If by is a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see .align() method). If an ndarray is passed, the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Split along rows (0) or columns (1).
  • level (int, level name, or sequence of such, default None) If the axis is a MultiIndex (hierarchical), group by a particularlevel or levels.
  • as_index (bool, default True) For aggregated output, return object with group labels as theindex. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output.
  • sort (bool, default True) Sort group keys. Get better performance by turning this off.Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.
  • group_keys (bool, default True) When calling apply, add group keys to index to identify pieces.
  • squeeze (bool, default False) Reduce the dimensionality of the return type if possible,otherwise return a consistent type.
    .. deprecated:: 1.1.0
  • observed (bool, default False) This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
  • dropna (bool, default True) If True, and if group keys contain NA values, NA values togetherwith row/column will be dropped. If False, NA values will also be treated as the key in groups
    .. versionadded:: 1.1.0
Returns (DataFrameGroupBy)

Returns a groupby object that contains information about the groups.

See Also

resample : Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide <https://pandas.pydata.org/pandas-docs/stable/groupby.html>__ for more.

Examples
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True:

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5
>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4
>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0
method

pivot(index=None, columns=None, values=None)

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the :ref:User Guide <reshaping> for more on reshaping.

Parameters
  • index (str or object or a list of str, optional) Column to use to make new frame's index. If None, usesexisting index.
    .. versionchanged:: 1.1.0 Also accept list of index names.
  • columns (str or object or a list of str) Column to use to make new frame's columns.
    .. versionchanged:: 1.1.0 Also accept list of columns names.
  • values (str, object or a list of the previous, optional) Column(s) to use for populating new frame's values. If notspecified, all remaining columns will be used and the result will have hierarchically indexed columns.
Returns (DataFrame)

Returns reshaped DataFrame.

Raises
  • ValueError When there are any index, columns combinations with multiplevalues. DataFrame.pivot_table when you need to aggregate.
See Also

DataFrame.pivottable : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.

Notes

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

Examples
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',...                            'two'],
...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
...                    'baz': [1, 2, 3, 4, 5, 6],
...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
    foo   bar  baz  zoo
0   one   A    1    x
1   one   B    2    y
2   one   C    3    z
3   two   A    4    q
4   two   B    5    w
5   two   C    6    t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar')['baz']
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>> df = pd.DataFrame({
...        "lev1": [1, 1, 1, 2, 2, 2],
...        "lev2": [1, 1, 2, 1, 1, 2],
...        "lev3": [1, 2, 1, 2, 1, 2],
...        "lev4": [1, 2, 3, 4, 5, 6],
...        "values": [0, 1, 2, 3, 4, 5]})
>>> df
    lev1 lev2 lev3 lev4 values
0   1    1    1    1    0
1   1    1    2    2    1
2   1    2    1    3    2
3   2    1    2    4    3
4   2    1    1    5    4
5   2    2    2    6    5
>>> df.pivot(index="lev1", columns=["lev2", "lev3"],values="values")
lev2    1         2
lev3    1    2    1    2
lev1
1     0.0  1.0  2.0  NaN
2     4.0  3.0  NaN  5.0
>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
      lev3    1    2
lev1  lev2
   1     1  0.0  1.0
         2  2.0  NaN
   2     1  4.0  3.0
         2  NaN  5.0

A ValueError is raised if there are any duplicates.

>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
...                    "bar": ['A', 'A', 'B', 'C'],
...                    "baz": [1, 2, 3, 4]})
>>> df
   foo bar  baz
0  one   A    1
1  one   A    2
2  two   B    3
3  two   C    4

Notice that the first two rows are the same for our index and columns arguments.

>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
   ...
ValueError: Index contains duplicate entries, cannot reshape
method

pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True)

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters
  • index (column, Grouper, array, or list of the previous) If an array is passed, it must be the same length as the data. Thelist can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.
  • columns (column, Grouper, array, or list of the previous) If an array is passed, it must be the same length as the data. Thelist can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.
  • aggfunc (function, list of functions, dict, default numpy.mean) If list of functions passed, the resulting pivot table will havehierarchical columns whose top level are the function names (inferred from the function objects themselves) If dict is passed, the key is column to aggregate and value is function or list of functions.
  • fill_value (scalar, default None) Value to replace missing values with (in the resulting pivot table,after aggregation).
  • margins (bool, default False) Add all row / columns (e.g. for subtotal / grand totals).
  • dropna (bool, default True) Do not include columns whose entries are all NaN.
  • margins_name (str, default 'All') Name of the row / column that will contain the totalswhen margins is True.
  • observed (bool, default False) This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
    .. versionchanged:: 0.25.0
  • sort (bool, default True) Specifies if the result should be sorted.
    .. versionadded:: 1.3.0
Returns (DataFrame)

An Excel style pivot table.

See Also

DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.melt: Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.

Examples
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9

This first example aggregates values by taking the sum.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum)
>>> table
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

We can also fill missing values using the fill_value parameter.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C        large  small
A   B
bar one      4      5
    two      7      6
foo one      4      1
    two      0      6

The next example aggregates by taking the mean across multiple columns.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': np.mean})
>>> table
                D         E
A   C
bar large  5.500000  7.500000
    small  5.500000  8.500000
foo large  2.000000  4.500000
    small  2.333333  4.333333

We can also calculate multiple types of aggregations for any given value column.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': [min, max, np.mean]})
>>> table
                D    E
            mean  max      mean  min
A   C
bar large  5.500000  9.0  7.500000  6.0
    small  5.500000  9.0  8.500000  8.0
foo large  2.000000  5.0  4.500000  4.0
    small  2.333333  6.0  4.333333  2.0
method

stack(level=-1, dropna=True)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

  • if the columns have a single level, the output is a Series;
  • if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.
Parameters
  • level (int, str, list, default -1) Level(s) to stack from the column axis onto the indexaxis, defined as one index or label, or a list of indices or labels.
  • dropna (bool, default True) Whether to drop rows in the resulting Frame/Series withmissing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.
Returns (DataFrame or Series)

Stacked dataframe or series.

See Also

DataFrame.unstack : Unstack prescribed level(s) from index axis onto column axis. DataFrame.pivot : Reshape dataframe from long format to wide format. DataFrame.pivottable : Create a spreadsheet-style pivot table as a DataFrame.

Notes

The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).

Examples

Single level columns

>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
...                                     index=['cat', 'dog'],
...                                     columns=['weight', 'height'])

Stacking a dataframe with a single level column axis returns a Series:

>>> df_single_level_cols
     weight height
cat       0      1
dog       2      3
>>> df_single_level_cols.stack()
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64

Multi level columns: simple case

>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol1)

Stacking a dataframe with a multi-level column axis:

>>> df_multi_level_cols1
     weight
         kg    pounds
cat       1        2
dog       2        4
>>> df_multi_level_cols1.stack()
            weight
cat kg           1
    pounds       2
dog kg           2
    pounds       4

Missing values

>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:

>>> df_multi_level_cols2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0
>>> df_multi_level_cols2.stack()
        height  weight
cat kg     NaN     1.0
    m      2.0     NaN
dog kg     NaN     3.0
    m      4.0     NaN

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0)
             kg    m
cat height  NaN  2.0
    weight  1.0  NaN
dog height  NaN  4.0
    weight  3.0  NaN
>>> df_multi_level_cols2.stack([0, 1])
cat  height  m     2.0
     weight  kg    1.0
dog  height  m     4.0
     weight  kg    3.0
dtype: float64

Dropping missing values

>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

Note that rows where all values are missing are dropped by default but this behaviour can be controlled via the dropna keyword parameter:

>>> df_multi_level_cols3
    weight height
        kg      m
cat    NaN    1.0
dog    2.0    3.0
>>> df_multi_level_cols3.stack(dropna=False)
        height  weight
cat kg     NaN     NaN
    m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
>>> df_multi_level_cols3.stack(dropna=True)
        height  weight
cat m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
method

explode(column, ignore_index=False)

Transform each element of a list-like to a row, replicating index values.

.. versionadded:: 0.25.0

Parameters
  • column (str or tuple or list thereof) Column(s) to explode.For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.
    .. versionadded:: 1.3.0 Multi-column explode
  • ignore_index (bool, default False) If True, the resulting index will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.1.0
Returns (DataFrame)

Exploded lists to rows of the subset columns;index will be duplicated for these rows.

Raises
  • ValueError
    • If columns of the frame are not unique.
    • If specified columns to explode is empty list.
    • If specified columns to explode have not matching count of elements rowwise in the frame.
See Also

DataFrame.unstack : Pivot a level of the (necessarily hierarchical) index labels. DataFrame.melt : Unpivot a DataFrame from wide format to long format. Series.explode : Explode a DataFrame from list-like columns to long format.

Notes

This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.

Examples
>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],...                    'B': 1,
...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode('A')
     A  B          C
0    0  1  [a, b, c]
0    1  1  [a, b, c]
0    2  1  [a, b, c]
1  foo  1        NaN
2  NaN  1         []
3    3  1     [d, e]
3    4  1     [d, e]

Multi-column explode.

>>> df.explode(list('AC'))
     A  B    C
0    0  1    a
0    1  1    b
0    2  1    c
1  foo  1  NaN
2  NaN  1  NaN
3    3  1    d
3    4  1    e
method

unstack(level=-1, fill_value=None)

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Parameters
  • level (int, str, or list of these, default -1 (last level)) Level(s) of index to unstack, can pass level name.
  • fill_value (int, str or dict) Replace NaN with this value if the unstack produces missing values.
See Also

DataFrame.pivot : Pivot a table based on column values.DataFrame.stack : Pivot a level of the column labels (inverse operation from unstack).

Examples
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),...                                    ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64
>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0
>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64
method

melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'.

Parameters
  • id_vars (tuple, list, or ndarray, optional) Column(s) to use as identifier variables.
  • value_vars (tuple, list, or ndarray, optional) Column(s) to unpivot. If not specified, uses all columns thatare not set as id_vars.
  • var_name (scalar) Name to use for the 'variable' column. If None it usesframe.columns.name or 'variable'.
  • value_name (scalar, default 'value') Name to use for the 'value' column.
  • col_level (int or str, optional) If columns are a MultiIndex then use this level to melt.
  • ignore_index (bool, default True) If True, original index is ignored. If False, the original index is retained.Index labels will be repeated as necessary.
    .. versionadded:: 1.1.0
Returns (DataFrame)

Unpivoted DataFrame.

See Also

melt : Identical method.pivot_table : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Return reshaped DataFrame organized by given index / column values. DataFrame.explode : Explode a DataFrame from list-like columns to long format.

Examples
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
3  a        C      2
4  b        C      4
5  c        C      6

The names of 'variable' and 'value' columns can be customized:

>>> df.melt(id_vars=['A'], value_vars=['B'],
...         var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

Original index values can be kept around:

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
0  a        C      2
1  b        C      4
2  c        C      6

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]
>>> df
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5
method

diff(periods=1, axis=0)

First discrete difference of element.

Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row).

Parameters
  • periods (int, default 1) Periods to shift for calculating difference, accepts negativevalues.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Take difference over rows (0) or columns (1).
Returns (Dataframe)

First differences of the Series.

See Also

Dataframe.pct_change: Percent change over given number of periods.Dataframe.shift: Shift index by desired number of periods with an optional time freq. Series.diff: First discrete difference of object.

Notes

For boolean dtypes, this uses :meth:operator.xor rather than :meth:operator.sub. The result is calculated according to current dtype in Dataframe, however dtype of the result is always float64.

Examples

w

, , ) f c 1 4 9 6 5 6

) c N 0 0 0 0 0

n

) c 0 3 7 3 0 8

w

) c N N N 0 0 0

w

) c 0 0 0 0 0 N

e

) ) a N 0

method

aggregate(func=None, axis=0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters
  • func (function, str, list or dict) Function to use for aggregating the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply.
    Accepted combinations are:
    • function
    • string function name
    • list of functions and/or function names, e.g. [np.sum, 'mean']
    • dict of axis labels -> functions, function names or list of such.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.
  • *args Positional arguments to pass to func.
  • **kwargs Keyword arguments to pass to func.
Returns (scalar, Series or DataFrame)

:

n n s

.

e m , d o .

.

See Also

DataFrame.apply : Perform any type of operations.DataFrame.transform : Perform transformation type operations. core.groupby.GroupBy : Perform operations over groups. core.resample.Resampler : Perform operations over resampled bins. core.window.Rolling : Perform operations over rolling window. core.window.Expanding : Perform operations over expanding window. core.window.ExponentialMovingWindow : Perform operation over exponential weighted window.

Notes

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples
>>> df = pd.DataFrame([[1, 2, 3],...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64
method

transform(func, axis=0, *args, **kwargs)

Call func on self producing a DataFrame with transformed values.

Produced DataFrame will have same axis length as self.

Parameters
  • func (function, str, list-like or dict-like) Function to use for transforming the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.
    Accepted combinations are:
    • function
    • string function name
    • list-like of functions and/or function names, e.g. [np.exp, 'sqrt']
    • dict-like of axis labels -> functions, function names or list-like of such.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.
  • *args Positional arguments to pass to func.
  • **kwargs Keyword arguments to pass to func.
Returns (DataFrame)

A DataFrame that must have the same length as self.

See Also

DataFrame.agg : Only perform aggregating type operations.DataFrame.apply : Invoke function on a DataFrame.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

Examples
>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056

You can call transform on a GroupBy object:

>>> df = pd.DataFrame({
...     "Date": [
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df
         Date  Data
0  2015-05-08     5
1  2015-05-07     8
2  2015-05-06     6
3  2015-05-05     1
4  2015-05-08    50
5  2015-05-07   100
6  2015-05-06    60
7  2015-05-05   120
>>> df.groupby('Date')['Data'].transform('sum')
0     55
1    108
2     66
3    121
4     55
5    108
6     66
7    121
Name: Data, dtype: int64
>>> df = pd.DataFrame({
...     "c": [1, 1, 1, 2, 2, 2, 2],
...     "type": ["m", "n", "o", "m", "m", "n", "n"]
... })
>>> df
   c type
0  1    m
1  1    n
2  1    o
3  2    m
4  2    m
5  2    n
6  2    n
>>> df['size'] = df.groupby('c')['type'].transform(len)
>>> df
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4
method

apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame's index (axis=0) or the DataFrame's columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

Parameters
  • func (function) Function to apply to each column or row.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis along which the function is applied:
    • 0 or 'index': apply function to each column.
    • 1 or 'columns': apply function to each row.
  • raw (bool, default False) Determines if row or column is passed as a Series or ndarray object:
    • False : passes each row or column as a Series to the function.
    • True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.
  • result_type ({'expand', 'reduce', 'broadcast', None}, default None) These only act when axis=1 (columns):
    • 'expand' : list-like results will be turned into columns.
    • 'reduce' : returns a Series if possible rather than expanding list-like results. This is the opposite of 'expand'.
    • 'broadcast' : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
    The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.
  • args (tuple) Positional arguments to pass to func in addition to thearray/series.
  • **kwargs Additional keyword arguments to pass as keywords arguments tofunc.
Returns (Series or DataFrame)

Result of applying func along the given axis of theDataFrame.

See Also

DataFrame.applymap: For elementwise operations.DataFrame.aggregate: Only perform aggregating type operations. DataFrame.transform: Only perform transforming type operations.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

Examples
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])>>> df
   A  B
0  4  9
1  4  9
2  4  9

Using a numpy universal function (in this case the same as np.sqrt(df)):

>>> df.apply(np.sqrt)
     A    B
0  2.0  3.0
1  2.0  3.0
2  2.0  3.0

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A    12
B    27
dtype: int64
>>> df.apply(np.sum, axis=1)
0    13
1    13
2    13
dtype: int64

Returning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
   0  1
0  1  2
1  1  2
2  1  2

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2

Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.

>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
   A  B
0  1  2
1  1  2
2  1  2
method

applymap(func, na_action=None, **kwargs)

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters
  • func (callable) Python function, returns a single value from a single value.
  • na_action ({None, 'ignore'}, default None) If ‘ignore’, propagate NaN values, without passing them to func.
    .. versionadded:: 1.2
  • **kwargs Additional keyword arguments to pass as keywords arguments tofunc.
    .. versionadded:: 1.3.0
Returns (DataFrame)

Transformed DataFrame.

See Also

DataFrame.apply : Apply a function along input axis of DataFrame.

Examples
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])>>> df
       0      1
0  1.000  2.120
1  3.356  4.567
>>> df.applymap(lambda x: len(str(x)))
   0  1
0  3  4
1  5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')
      0  1
0  <NA>  4
1     5  5

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.applymap(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it's better to avoid applymap in that case.

>>> df ** 2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489
method

append(other, ignore_index=False, verify_integrity=False, sort=False)

Append rows of other to the end of caller, returning a new object.

Columns in other that are not in the caller are added as new columns.

Parameters
  • other (DataFrame or Series/dict-like object, or list of these) The data to append.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
  • verify_integrity (bool, default False) If True, raise ValueError on creating index with duplicates.
  • sort (bool, default False) Sort columns if the columns of self and other are not aligned.
    .. versionchanged:: 1.0.0
    Changed to not sort by default.
    
Returns (DataFrame)

A new DataFrame consisting of the rows of caller and the rows of other.

See Also

concat : General function to concatenate DataFrame or Series objects.

Notes

If a list of dict/series is passed and the keys are all contained in the DataFrame's index, the order of the columns in the resulting DataFrame will be unchanged.

Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.

Examples
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'), index=['x', 'y'])>>> df
   A  B
x  1  2
y  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'), index=['x', 'y'])
>>> df.append(df2)
   A  B
x  1  2
y  3  4
x  5  6
y  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources.

Less efficient:

>>> df = pd.DataFrame(columns=['A'])
>>> for i in range(5):
...     df = df.append({'A': i}, ignore_index=True)
>>> df
   A
0  0
1  1
2  2
3  3
4  4

More efficient:

>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
...           ignore_index=True)
   A
0  0
1  1
2  2
3  3
4  4
method

join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)

Join columns of another DataFrame.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters
  • other (DataFrame, Series, or list of DataFrame) Index should be similar to one of the columns in this one. If aSeries is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.
  • on (str, list of str, or array-like, optional) Column or index level name(s) in the caller to join on the indexin other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.
  • how ({'left', 'right', 'outer', 'inner'}, default 'left') How to handle the operation of the two objects.
    • left: use calling frame's index (or column if on is specified)
    • right: use other's index.
    • outer: form union of calling frame's index (or column if on is specified) with other's index, and sort it. lexicographically.
    • inner: form intersection of calling frame's index (or column if on is specified) with other's index, preserving the order of the calling's one.
  • lsuffix (str, default '') Suffix to use from left frame's overlapping columns.
  • rsuffix (str, default '') Suffix to use from right frame's overlapping columns.
  • sort (bool, default False) Order result DataFrame lexicographically by the join key. If False,the order of the join key depends on the join type (how keyword).
Returns (DataFrame)

A dataframe containing columns from both the caller and other.

See Also

DataFrame.merge : For column(s)-on-column(s) operations.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Support for specifying index levels as the on parameter was added in version 0.23.0.

Examples
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
  key   A
0  K0  A0
1  K1  A1
2  K2  A2
3  K3  A3
4  K4  A4
5  K5  A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})
>>> other
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix='_caller', rsuffix='_other')
  key_caller   A key_other    B
0         K0  A0        K0   B0
1         K1  A1        K1   B1
2         K2  A2        K2   B2
3         K3  A3       NaN  NaN
4         K4  A4       NaN  NaN
5         K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index('key').join(other.set_index('key'))
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other's index but we can use any column in df. This method preserves the original DataFrame's index in the result.

>>> df.join(other.set_index('key'), on='key')
  key   A    B
0  K0  A0   B0
1  K1  A1   B1
2  K2  A2   B2
3  K3  A3  NaN
4  K4  A4  NaN
5  K5  A5  NaN
method

merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

Merge DataFrame or named Series objects with a database-style join.

A named Series object is treated as a DataFrame with a single named column.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Parameters
  • right (DataFrame or named Series) Object to merge with.
  • how ({'left', 'right', 'outer', 'inner', 'cross'}, default 'inner') Type of merge to be performed.
    • left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
    • right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
    • outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
    • inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
    • cross: creates the cartesian product from both frames, preserves the order of the left keys.
    .. versionadded:: 1.2.0
  • on (label or list) Column or index level names to join on. These must be found in bothDataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.
  • left_on (label or list, or array-like) Column or index level names to join on in the left DataFrame. Can alsobe an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.
  • right_on (label or list, or array-like) Column or index level names to join on in the right DataFrame. Can alsobe an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.
  • left_index (bool, default False) Use the index from the left DataFrame as the join key(s). If it is aMultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.
  • right_index (bool, default False) Use the index from the right DataFrame as the join key. Same caveats asleft_index.
  • sort (bool, default False) Sort the join keys lexicographically in the result DataFrame. If False,the order of the join keys depends on the join type (how keyword).
  • suffixes (list-like, default is ("_x", "_y")) A length-2 sequence where each element is optionally a stringindicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.
  • copy (bool, default True) If False, avoid copy if possible.
  • indicator (bool or str, default False) If True, adds a column to the output DataFrame called "_merge" withinformation on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of "left_only" for observations whose merge key only appears in the left DataFrame, "right_only" for observations whose merge key only appears in the right DataFrame, and "both" if the observation's merge key is found in both DataFrames.
  • validate (str, optional) If specified, checks if merge is of specified type.
    • "one_to_one" or "1:1": check if merge keys are unique in both left and right datasets.
    • "one_to_many" or "1:m": check if merge keys are unique in left dataset.
    • "many_to_one" or "m:1": check if merge keys are unique in right dataset.
    • "many_to_many" or "m:m": allowed, but does not result in checks.
Returns (DataFrame)

A DataFrame of the two merged objects.

See Also

merge_ordered : Merge with optional filling/interpolation.merge_asof : Merge on nearest keys. DataFrame.join : Similar method using indices.

Notes

Support for specifying index levels as the on, left_on, and right_on parameters was added in version 0.23.0 Support for merging named Series objects was added in version 0.24.0

Examples
>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],...                     'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
>>> df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  foo        5  foo        5
3  foo        5  foo        8
4  bar        2  bar        6
5  baz        3  baz        7

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',
...           suffixes=('_left', '_right'))
  lkey  value_left rkey  value_right
0  foo           1  foo            5
1  foo           1  foo            8
2  foo           5  foo            5
3  foo           5  foo            8
4  bar           2  bar            6
5  baz           3  baz            7

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
    Index(['value'], dtype='object')
>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
      a  b
0   foo  1
1   bar  2
>>> df2
      a  c
0   foo  3
1   baz  4
>>> df1.merge(df2, how='inner', on='a')
      a  b  c
0   foo  1  3
>>> df1.merge(df2, how='left', on='a')
      a  b  c
0   foo  1  3.0
1   bar  2  NaN
>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
    left
0   foo
1   bar
>>> df2
    right
0   7
1   8
>>> df1.merge(df2, how='cross')
   left  right
0   foo      7
1   foo      8
2   bar      7
3   bar      8
method

round(decimals=0, *args, **kwargs)

Round a DataFrame to a variable number of decimal places.

Parameters
  • decimals (int, dict, Series) Number of decimal places to round each column to. If an int isgiven, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.
  • *args Additional keywords have no effect but might be accepted forcompatibility with numpy.
  • **kwargs Additional keywords have no effect but might be accepted forcompatibility with numpy.
Returns (DataFrame)

A DataFrame with the affected columns rounded to the specifiednumber of decimal places.

See Also

numpy.around : Round a numpy array to the given number of decimals.Series.round : Round a Series to the given number of decimals.

Examples
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],...                   columns=['dogs', 'cats'])
>>> df
    dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18

By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({'dogs': 1, 'cats': 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0
method

corr(method='pearson', min_periods=1)

Compute pairwise correlation of columns, excluding NA/null values.

Parameters
  • method ({'pearson', 'kendall', 'spearman'} or callable) Method of correlation:
    • pearson : standard correlation coefficient
    • kendall : Kendall Tau correlation coefficient
    • spearman : Spearman rank correlation
    • callable: callable with input two 1d ndarrays and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable's behavior.
  • min_periods (int, optional) Minimum number of observations required per pair of columnsto have a valid result.
Returns (DataFrame)

Correlation matrix.

See Also

DataFrame.corrwith : Compute pairwise correlation with another DataFrame or Series. Series.corr : Compute the correlation between two Series.

Examples
>>> def histogram_intersection(a, b):...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0
method

cov(min_periods=None, ddof=1)

Compute pairwise covariance of columns, excluding NA/null values.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix <https://en.wikipedia.org/wiki/Covariance_matrix>__ of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters
  • min_periods (int, optional) Minimum number of observations required per pair of columnsto have a valid result.
  • ddof (int, default 1) Delta degrees of freedom. The divisor used in calculationsis N - ddof, where N represents the number of elements.
    .. versionadded:: 1.1.0
Returns (DataFrame)

The covariance matrix of the series of the DataFrame.

See Also

Series.cov : Compute covariance with another Series.core.window.ExponentialMovingWindow.cov: Exponential weighted sample covariance. core.window.Expanding.cov : Expanding sample covariance. core.window.Rolling.cov : Rolling sample covariance.

Notes

Returns the covariance matrix of the DataFrame's time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random <https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>__) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices <https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_ matrices>__ for more details.

Examples
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],...                   columns=['dogs', 'cats'])
>>> df.cov()
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202
method

corrwith(other, axis=0, drop=False, method='pearson')

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Parameters
  • other (DataFrame, Series) Object with which to compute correlations.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' to compute column-wise, 1 or 'columns' forrow-wise.
  • drop (bool, default False) Drop missing indices from result.
  • method ({'pearson', 'kendall', 'spearman'} or callable) Method of correlation:
    • pearson : standard correlation coefficient
    • kendall : Kendall Tau correlation coefficient
    • spearman : Spearman rank correlation
    • callable: callable with input two 1d ndarrays and returning a float.
Returns (Series)

Pairwise correlations.

See Also

DataFrame.corr : Compute pairwise correlation of columns.

method

count(axis=0, level=None, numeric_only=False)

Count non-NA cells for each column or row.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index' counts are generated for each column.If 1 or 'columns' counts are generated for each row.
  • level (int or str, optional) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a DataFrame. A str specifies the level name.
  • numeric_only (bool, default False) Include only float, int or boolean data.
Returns (Series or DataFrame)

For each column/row the number of non-NA/null entries.If level is specified returns a DataFrame.

See Also

Series.count: Number of non-NA elements in a Series.DataFrame.value_counts: Count unique combinations of columns. DataFrame.shape: Number of DataFrame rows and columns (including NA elements). DataFrame.isna: Boolean same-sized DataFrame showing places of NA elements.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis='columns')
0    3
1    2
2    3
3    3
4    3
dtype: int64
method

nunique(axis=0, dropna=True)

Count number of distinct elements in specified axis.

Return Series with number of distinct elements. Can ignore NaN values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' forcolumn-wise.
  • dropna (bool, default True) Don't include NaN in the counts.
See Also

Series.nunique: Method nunique for Series.DataFrame.count: Count non-NA cells for each column or row.

Examples
>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})>>> df.nunique()
A    3
B    2
dtype: int64
>>> df.nunique(axis=1)
0    1
1    2
2    2
dtype: int64
method

idxmin(axis=0, skipna=True)

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series)

Indexes of minima along the specified axis.

Raises
  • ValueError
    • If the row/column is empty
See Also

Series.idxmin : Return index of the minimum element.

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object
method

idxmax(axis=0, skipna=True)

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series)

Indexes of maxima along the specified axis.

Raises
  • ValueError
    • If the row/column is empty
See Also

Series.idxmax : Return index of the maximum element.

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object
method

mode(axis=0, numeric_only=False, dropna=True)

Get the mode(s) of each element along the selected axis.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to iterate over while searching for the mode:
    • 0 or 'index' : get mode of each column
    • 1 or 'columns' : get mode of each row.
  • numeric_only (bool, default False) If True, only apply to numeric columns.
  • dropna (bool, default True) Don't consider counts of NaN/NaT.
Returns (DataFrame)

The modes of each column or row.

See Also

Series.mode : Return the highest frequency value in a Series.Series.value_counts : Return the counts of values in a Series.

Examples
>>> df = pd.DataFrame([('bird', 2, 2),...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN.

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis='columns', numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN
method

quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Return values at the given quantile over requested axis.

Parameters
  • q (float or array-like, default 0.5 (50% quantile)) Value between 0 <= q <= 1, the quantile(s) to compute.
  • axis ({0, 1, 'index', 'columns'}, default 0) Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • numeric_only (bool, default True) If False, the quantile of datetime and timedelta data will becomputed as well.
  • interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) This optional parameter specifies the interpolation method to use,when the desired quantile lies between two data points i and j:
    • linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
    • lower: i.
    • higher: j.
    • nearest: i or j whichever is nearest.
    • midpoint: (i + j) / 2.
Returns (Series or DataFrame)

ee . e .

See Also

core.window.Rolling.quantile: Rolling quantile.numpy.percentile: Numpy function to compute the percentile.

Examples
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),...                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

>>> df = pd.DataFrame({'A': [1, 2],
...                    'B': [pd.Timestamp('2010'),
...                          pd.Timestamp('2011')],
...                    'C': [pd.Timedelta('1 days'),
...                          pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object
method

asfreq(freq, method=None, how=None, normalize=False, fill_value=None)

Convert time series to specified frequency.

Returns the original data conformed to a new index with the specified frequency.

If the index of this DataFrame is a :class:~pandas.PeriodIndex, the new index is the result of transforming the original index with :meth:PeriodIndex.asfreq <pandas.PeriodIndex.asfreq> (so the original index will map one-to-one to the new index).

Otherwise, the new index will be equivalent to pd.date_range(start, end, freq=freq) where start and end are, respectively, the first and last entries in the original index (see :func:pandas.date_range). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see the method parameter below).

The :meth:resample method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.

Parameters
  • freq (DateOffset or str) Frequency DateOffset or string.
  • method ({'backfill'/'bfill', 'pad'/'ffill'}, default None) Method to use for filling holes in reindexed Series (note thisdoes not fill NaNs that already were present):
    • 'pad' / 'ffill': propagate last valid observation forward to next valid
    • 'backfill' / 'bfill': use NEXT valid observation to fill.
  • how ({'start', 'end'}, default end) For PeriodIndex only (see PeriodIndex.asfreq).
  • normalize (bool, default False) Whether to reset output index to midnight.
  • fill_value (scalar, optional) Value to use for missing values, applied during upsampling (notethis does not fill NaNs that already were present).
Returns (DataFrame)

DataFrame object reindexed to the specified frequency.

See Also

reindex : Conform DataFrame to new index with optional filling logic.

Notes

To learn more about the frequency strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s': series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30S', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30S', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0
method

resample(rule, axis=0, closed=None, label=None, convention='start', kind=None, loffset=None, base=None, on=None, level=None, origin='start_day', offset=None)

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the on/level keyword parameter.

Parameters
  • rule (DateOffset, Timedelta or str) The offset string or object representing target conversion.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Which axis to use for up- or down-sampling. For Series thiswill default to 0, i.e. along the rows. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.
  • closed ({'right', 'left'}, default None) Which side of bin interval is closed. The default is 'left'for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'.
  • label ({'right', 'left'}, default None) Which bin edge label to label bucket with. The default is 'left'for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'.
  • convention ({'start', 'end', 's', 'e'}, default 'start') For PeriodIndex only, controls whether to use the start orend of rule.
  • kind ({'timestamp', 'period'}, optional, default None) Pass 'timestamp' to convert the resulting index to aDateTimeIndex or 'period' to convert it to a PeriodIndex. By default the input representation is retained.
  • loffset (timedelta, default None) Adjust the resampled time labels.
    .. deprecated:: 1.1.0 You should add the loffset to the df.index after the resample. See below.
  • base (int, default 0) For frequencies that evenly subdivide 1 day, the "origin" of theaggregated intervals. For example, for '5min' frequency, base could range from 0 through 4. Defaults to 0.
    .. deprecated:: 1.1.0 The new arguments that you should use are 'offset' or 'origin'.
  • on (str, optional) For a DataFrame, column to use instead of index for resampling.Column must be datetime-like.
  • level (str or int, optional) For a MultiIndex, level (name or number) to use forresampling. level must be datetime-like.
  • origin ({'epoch', 'start', 'start_day', 'end', 'end_day'}, Timestamp) or str, default 'start_day'The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a timestamp is not used, these values are also supported:
    • 'epoch': origin is 1970-01-01
    • 'start': origin is the first value of the timeseries
    • 'start_day': origin is the first day at midnight of the timeseries
    .. versionadded:: 1.1.0
    • 'end': origin is the last value of the timeseries
    • 'end_day': origin is the ceiling midnight of the last day
    .. versionadded:: 1.3.0
  • offset (Timedelta or str, default is None) An offset timedelta added to the origin.
    .. versionadded:: 1.1.0
Returns (pandas.core.Resampler)

:class:~pandas.core.Resampler object.

See Also

Series.resample : Resample a Series.DataFrame.resample : Resample a DataFrame. groupby : Group DataFrame by mapping, function, label, or list of labels. asfreq : Reindex a DataFrame with the given frequency without grouping.

Notes

See the user guide <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>__ for more.

To learn more about the offset strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>__.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: T, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample('3T').sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one.

>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but close the right side of the bin interval.

>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3T, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample('30S').asfreq()[0:5]   # Select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30S, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the pad method.

>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(arraylike):
...     return np.sum(arraylike) + 5
...
>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3T, dtype: int64

For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.

Resample a year by quarter using 'start' convention. Values are assigned to the first quarter of the period.

>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
...                                             freq='A',
...                                             periods=2))
>>> s
2012    1
2013    2
Freq: A-DEC, dtype: int64
>>> s.resample('Q', convention='start').asfreq()
2012Q1    1.0
2012Q2    NaN
2012Q3    NaN
2012Q4    NaN
2013Q1    2.0
2013Q2    NaN
2013Q3    NaN
2013Q4    NaN
Freq: Q-DEC, dtype: float64

Resample quarters by month using 'end' convention. Values are assigned to the last month of the period.

>>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',
...                                                   freq='Q',
...                                                   periods=4))
>>> q
2018Q1    1
2018Q2    2
2018Q3    3
2018Q4    4
Freq: Q-DEC, dtype: int64
>>> q.resample('M', convention='end').asfreq()
2018-03    1.0
2018-04    NaN
2018-05    NaN
2018-06    2.0
2018-07    NaN
2018-08    NaN
2018-09    3.0
2018-10    NaN
2018-11    NaN
2018-12    4.0
Freq: M, dtype: float64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
...                                     periods=8,
...                                     freq='W')
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
>>> df.resample('M', on='week_starting').mean()
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df2 = pd.DataFrame(
...     d2,
...     index=pd.MultiIndex.from_product(
...         [days, ['morning', 'afternoon']]
...     )
... )
>>> df2
                      price  volume
2000-01-01 morning       10      50
           afternoon     11      60
2000-01-02 morning        9      40
           afternoon     13     100
2000-01-03 morning       14      50
           afternoon     18     100
2000-01-04 morning       17      40
           afternoon     19      50
>>> df2.resample('D', level=0).sum()
            price  volume
2000-01-01     21     110
2000-01-02     22     140
2000-01-03     32     150
2000-01-04     36      90

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7T, dtype: int64
>>> ts.resample('17min').sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17T, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64

If you want to take the largest Timestamp as the end of the bins:

>>> ts.resample('17min', origin='end').sum()
2000-10-01 23:35:00     0
2000-10-01 23:52:00    18
2000-10-02 00:09:00    27
2000-10-02 00:26:00    63
Freq: 17T, dtype: int64

In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

>>> ts.resample('17min', origin='end_day').sum()
2000-10-01 23:38:00     3
2000-10-01 23:55:00    15
2000-10-02 00:12:00    45
2000-10-02 00:29:00    45
Freq: 17T, dtype: int64

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2:

>>> ts.resample('17min', offset='2min').sum()
2000-10-01 23:16:00     0
2000-10-01 23:33:00     9
2000-10-01 23:50:00    36
2000-10-02 00:07:00    39
2000-10-02 00:24:00    24
Freq: 17T, dtype: int64

To replace the use of the deprecated loffset argument:

>>> from pandas.tseries.frequencies import to_offset
>>> loffset = '19min'
>>> ts_out = ts.resample('17min').sum()
>>> ts_out.index = ts_out.index + to_offset(loffset)
>>> ts_out
2000-10-01 23:33:00     0
2000-10-01 23:50:00     9
2000-10-02 00:07:00    21
2000-10-02 00:24:00    54
2000-10-02 00:41:00    24
Freq: 17T, dtype: int64
method

to_timestamp(freq=None, how='start', axis=0, copy=True)

Cast to DatetimeIndex of timestamps, at beginning of period.

Parameters
  • freq (str, default frequency of PeriodIndex) Desired frequency.
  • how ({'s', 'e', 'start', 'end'}) Convention for converting period to timestamp; start of periodvs. end.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to convert (the index by default).
  • copy (bool, default True) If False then underlying input data is not copied.
method

to_period(freq=None, axis=0, copy=True)

Convert DataFrame from DatetimeIndex to PeriodIndex.

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).

Parameters
  • freq (str, default) Frequency of the PeriodIndex.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to convert (the index by default).
  • copy (bool, default True) If False then underlying input data is not copied.
method

isin(values)

Whether each element in the DataFrame is contained in values.

Parameters
  • values (iterable, Series, DataFrame or dict) The result will only be true at a location if all thelabels match. If values is a Series, that's the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.
Returns (DataFrame)

DataFrame of booleans showing whether each element in the DataFrameis contained in values.

See Also

DataFrame.eq: Equality test for DataFrame.Series.isin: Equivalent method on Series. Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},...                   index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that 'falcon' does not match based on the number of legs in df2.

>>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
...                      index=['spider', 'falcon'])
>>> df.isin(other)
        num_legs  num_wings
falcon      True       True
dog        False      False
method

copy(deep=True, copy_grouped=False)DataFrameGroupByABC

Copy the dataframe and keep the class

class

datar.core.grouped.DataFrameGroupBy(data, _group_vars=None, _group_drop=None, _group_data=None, **kwargs)

Bases
datar.core.grouped.DataFrameGroupByABC pandas.core.frame.DataFrame pandas.core.generic.NDFrame pandas.core.base.PandasObject pandas.core.accessor.DirNamesMixin pandas.core.indexing.IndexingMixin pandas.core.arraylike.OpsMixin

A customized DataFrameGroupBy class, other than pandas' DataFrameGroupBy

Pandas' DataFrameGroupBy has obj refer to the original data frame. We do it the reverse way by attaching the groupby object to the frame. So that it is: 1. easier to write single dispatch functions, as DataFrameGroupBy is now a subclass of pandas' DataFrame 2. easier to display the frame. We can use all utilities for frame to display. By core._frame_format_patch.py, we are also able to show the grouping information 3. possible for future optimizations

Known Issues: - Due to https://github.com/pandas-dev/pandas/issues/35202 Currently dropna is fixed to True of df.groupby(...) So no NAs will be kept in group vars - _drop = FALSE does not work when there are multiple group vars - Since group vars are required in DataFrame.groupby(), so virtual groupings are not supported. - Groupby on a column with tuples creates a multiindex https://github.com/pandas-dev/pandas/issues/21340 - Order of group data/groups does not follow the categories/levels of a category group variable.

Parameters
  • data (any) Data that used to construct the frame.
  • _group_vars (Union(str, str_, iterable of str or str_), optional) The grouping variables
  • _group_drop (bool, optional) Whether to drop non-observable rows
  • _group_data (DataFrame, optional) In most cases, used for copy. Use this groupdataif provided to avoid recalculate.
  • **kwargs (any) Additional keyword arguments passed to DataFrame constructor.
Attributes
  • _grouped_df The grouped data frame (pandas' DataFrameGroupBy object)
  • at (_AtIndexer) Access a single value for a row/column label pair.
    Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series. </>
  • attrs (dict) Dictionary of global attributes of this dataset.
    .. warning::
    attrs is experimental and may change without warning. </>
  • axes (list) Return a list representing the axes of the DataFrame.
    It has the row axis labels and column axis labels as the only members. They are returned in that order. </>
  • dtypes Return the dtypes in the DataFrame.
    This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the object dtype. See :ref:the User Guide <basics.dtypes> for more. </>
  • empty Indicator whether DataFrame is empty.
    True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0. </>
  • flags (Flags) Get the properties associated with this pandas object.
    The available flags are
    • :attr:Flags.allows_duplicate_labels
    </>
  • iat (_iAtIndexer) Access a single value for a row/column pair by integer position.
    Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series. </>
  • iloc (_iLocIndexer) Purely integer-location based indexing for selection by position.
    .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.
    Allowed inputs are:
    • An integer, e.g. 5.
    • A list or array of integers, e.g. [4, 3, 0].
    • A slice object with ints, e.g. 1:7.
    • A boolean array.
    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don't have a reference to the calling object, but would like to base your selection on some value.
    .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).
    See more at :ref:Selection by Position <indexing.integer>. </>
  • loc (_LocIndexer) Access a group of rows and columns by label(s) or a boolean array.
    .loc[] is primarily label based, but may also be used with a boolean array.
    Allowed inputs are:
    • A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
    • A list or array of labels, e.g. ['a', 'b', 'c'].
    • A slice object with labels, e.g. 'a':'f'.
    .. warning:: Note that contrary to usual python slices, both the start and the stop are included
    • A boolean array of the same length as the axis being sliced, e.g. [True, False, True].
    • An alignable boolean Series. The index of the key will be aligned before masking.
    • An alignable Index. The Index of the returned selection will be the input.
    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)
    See more at :ref:Selection by Label <indexing.label>. </>
  • ndim (int) Return an int representing the number of axes / array dimensions.
    Return 1 if Series. Otherwise return 2 if DataFrame. </>
  • shape (tuple) Return a tuple representing the dimensionality of the DataFrame.</>
  • size (int) Return an int representing the number of elements in this object.
    Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. </>
  • style (Styler) Returns a Styler object.
    Contains methods for building a styled HTML representation of the DataFrame. </>
  • values Return a Numpy representation of the DataFrame.
    .. warning::
    We recommend using :meth:DataFrame.to_numpy instead.
    Only the values in the DataFrame will be returned, the axes labels will be removed. </>
Methods
  • __array_wrap__(result, context) Gets called after a ufunc and other functions.</>
  • __contains__(key) (bool) True if the key is in the info axis</>
  • __delitem__(key) Delete item</>
  • __dir__() (list) Provide method name lookup and completion.</>
  • __finalize__(other, method, **kwargs) (FrameOrSeries) Propagate metadata from other to self.</>
  • __getattr__(name) After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use. </>
  • __iter__() (iterator) Iterate over info axis.</>
  • __len__() (int) Returns length of info axis, but here we use the index.</>
  • __matmul__(other) (DataFrame or Series) Matrix multiplication using binary @ operator in Python>=3.5.</>
  • __repr__() (str) Return a string representation for a particular DataFrame.</>
  • __rmatmul__(other) Matrix multiplication using binary @ operator in Python>=3.5.</>
  • __setattr__(name, value) After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use. </>
  • __sizeof__() (int) Generates the total memory usage for an object that returnseither a value or Series of values </>
  • abs() (abs) Return a Series/DataFrame with absolute numeric value of each element.</>
  • add_prefix(prefix) (Series or DataFrame) Prefix labels with string prefix.</>
  • add_suffix(suffix) (Series or DataFrame) Suffix labels with string suffix.</>
  • aggregate(func, axis, *args, **kwargs) (scalar, Series or DataFrame) Aggregate using one or more operations over the specified axis.</>
  • align(other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis) (left, right) : (DataFrame, type of other) Align two objects on their axes with the specified join method.</>
  • all(axis, bool_only, skipna, level, **kwargs) (Series or DataFrame) Return whether all elements are True, potentially over an axis.</>
  • any(axis, bool_only, skipna, level, **kwargs) (Series or DataFrame) Return whether any element is True, potentially over an axis.</>
  • append(other, ignore_index, verify_integrity, sort) (DataFrame) Append rows of other to the end of caller, returning a new object.</>
  • apply(func, axis, raw, result_type, args, **kwargs) (Series or DataFrame) Apply a function along an axis of the DataFrame.</>
  • applymap(func, na_action, **kwargs) (DataFrame) Apply a function to a Dataframe elementwise.</>
  • asfreq(freq, method, how, normalize, fill_value) (DataFrame) Convert time series to specified frequency.</>
  • asof(where, subset) (scalar, Series, or DataFrame) Return the last row(s) without any NaNs before where.</>
  • assign(**kwargs) (DataFrame) Assign new columns to a DataFrame.</>
  • astype(dtype, copy, errors) (casted : same type as caller) Cast a pandas object to a specified dtype dtype.</>
  • at_time(time, asof, axis) (Series or DataFrame) Select values at particular time of day (e.g., 9:30AM).</>
  • between_time(start_time, end_time, include_start, include_end, axis) (Series or DataFrame) Select values between particular times of the day (e.g., 9:00-9:30 AM).</>
  • bfill(axis, inplace, limit, downcast) (Series/DataFrame or None) Synonym for :meth:DataFrame.fillna with method='bfill'.</>
  • bool() (bool) Return the bool of a single element Series or DataFrame.</>
  • combine(other, func, fill_value, overwrite) (DataFrame) Perform column-wise combine with another DataFrame.</>
  • combine_first(other) (DataFrame) Update null elements with value in the same location in other.</>
  • compare(other, align_axis, keep_shape, keep_equal) (DataFrame) Compare to another DataFrame and show the differences.</>
  • convert_dtypes(infer_objects, convert_string, convert_integer, convert_boolean, convert_floating) (Series or DataFrame) Convert columns to best possible dtypes using dtypes supporting pd.NA.</>
  • copy(deep, copy_grouped) (DataFrameGroupByABC) Copy the dataframe and keep the class</>
  • corr(method, min_periods) (DataFrame) Compute pairwise correlation of columns, excluding NA/null values.</>
  • corrwith(other, axis, drop, method) (Series) Compute pairwise correlation.</>
  • count(axis, level, numeric_only) (Series or DataFrame) Count non-NA cells for each column or row.</>
  • cov(min_periods, ddof) (DataFrame) Compute pairwise covariance of columns, excluding NA/null values.</>
  • cummax(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative maximum over a DataFrame or Series axis.</>
  • cummin(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative minimum over a DataFrame or Series axis.</>
  • cumprod(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative product over a DataFrame or Series axis.</>
  • cumsum(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative sum over a DataFrame or Series axis.</>
  • describe(percentiles, include, exclude, datetime_is_numeric) (Series or DataFrame) Generate descriptive statistics.</>
  • diff(periods, axis) (Dataframe) First discrete difference of element.</>
  • dot(other) (Series or DataFrame) Compute the matrix multiplication between the DataFrame and other.</>
  • drop(labels, axis, index, columns, level, inplace, errors) (DataFrame or None) Drop specified labels from rows or columns.</>
  • drop_duplicates(subset, keep, inplace, ignore_index) (DataFrame or None) Return DataFrame with duplicate rows removed.</>
  • droplevel(level, axis) (Series/DataFrame) Return Series/DataFrame with requested index / column level(s) removed.</>
  • dropna(axis, how, thresh, subset, inplace) (DataFrame or None) Remove missing values.</>
  • duplicated(subset, keep) (Series) Return boolean Series denoting duplicate rows.</>
  • equals(other) (bool) Test whether two objects contain the same elements.</>
  • eval(expr, inplace, **kwargs) (ndarray, scalar, pandas object, or None) Evaluate a string describing operations on DataFrame columns.</>
  • ewm(com, span, halflife, alpha, min_periods, adjust, ignore_na, axis, times) (DataFrame) Provide exponential weighted (EW) functions.</>
  • expanding(min_periods, center, axis, method) (a Window sub-classed for the particular operation) Provide expanding transformations.</>
  • explode(column, ignore_index) (DataFrame) Transform each element of a list-like to a row, replicating index values.</>
  • ffill(axis, inplace, limit, downcast) (Series/DataFrame or None) Synonym for :meth:DataFrame.fillna with method='ffill'.</>
  • fillna(value, method, axis, inplace, limit, downcast) (DataFrame or None) Fill NA/NaN values using the specified method.</>
  • filter(items, like, regex, axis) (same type as input object) Subset the dataframe rows or columns according to the specified index labels.</>
  • first(offset) (Series or DataFrame) Select initial periods of time series data based on a date offset.</>
  • first_valid_index() (scalar : type of index) Return index for first non-NA value or None, if no NA value is found.</>
  • from_dict(data, orient, dtype, columns) (DataFrame) Construct DataFrame from dict of array-like or dicts.</>
  • from_records(data, index, exclude, columns, coerce_float, nrows) (DataFrame) Convert structured or record ndarray to DataFrame.</>
  • get(key, default) (value : same type as items contained in object) Get item from object for given key (ex: DataFrame column).</>
  • groupby(by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna) (DataFrameGroupBy) Group DataFrame using a mapper or by a Series of columns.</>
  • head(n) (same type as caller) Return the first n rows.</>
  • idxmax(axis, skipna) (Series) Return index of first occurrence of maximum over requested axis.</>
  • idxmin(axis, skipna) (Series) Return index of first occurrence of minimum over requested axis.</>
  • infer_objects() (converted : same type as input object) Attempt to infer better dtypes for object columns.</>
  • info(verbose, buf, max_cols, memory_usage, show_counts, null_counts) (None) Print a concise summary of a DataFrame.</>
  • insert(loc, column, value, allow_duplicates) Insert column into DataFrame at specified location.</>
  • isin(values) (DataFrame) Whether each element in the DataFrame is contained in values.</>
  • isna() (DataFrame) Detect missing values.</>
  • isnull() (DataFrame) Detect missing values.</>
  • items() (label : object) Iterate over (column name, Series) pairs.</>
  • iteritems() (label : object) Iterate over (column name, Series) pairs.</>
  • iterrows() (index : label or tuple of label) Iterate over DataFrame rows as (index, Series) pairs.</>
  • itertuples(index, name) (iterator) Iterate over DataFrame rows as namedtuples.</>
  • join(other, on, how, lsuffix, rsuffix, sort) (DataFrame) Join columns of another DataFrame.</>
  • keys() (Index) Get the 'info axis' (see Indexing for more).</>
  • kurt(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased kurtosis over requested axis.</>
  • last(offset) (Series or DataFrame) Select final periods of time series data based on a date offset.</>
  • last_valid_index() (scalar : type of index) Return index for last non-NA value or None, if no NA value is found.</>
  • lookup(row_labels, col_labels) (numpy.ndarray) Label-based "fancy indexing" function for DataFrame.Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. </>
  • mad(axis, skipna, level) (Series or DataFrame (if level specified)) Return the mean absolute deviation of the values over the requested axis.</>
  • max(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the maximum of the values over the requested axis.</>
  • mean(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the mean of the values over the requested axis.</>
  • median(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the median of the values over the requested axis.</>
  • melt(id_vars, value_vars, var_name, value_name, col_level, ignore_index) (DataFrame) Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.</>
  • memory_usage(index, deep) (Series) Return the memory usage of each column in bytes.</>
  • merge(right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate) (DataFrame) Merge DataFrame or named Series objects with a database-style join.</>
  • min(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the minimum of the values over the requested axis.</>
  • mode(axis, numeric_only, dropna) (DataFrame) Get the mode(s) of each element along the selected axis.</>
  • nlargest(n, columns, keep) (DataFrame) Return the first n rows ordered by columns in descending order.</>
  • notna() (DataFrame) Detect existing (non-missing) values.</>
  • notnull() (DataFrame) Detect existing (non-missing) values.</>
  • nsmallest(n, columns, keep) (DataFrame) Return the first n rows ordered by columns in ascending order.</>
  • nunique(axis, dropna) (Series) Count number of distinct elements in specified axis.</>
  • pct_change(periods, fill_method, limit, freq, **kwargs) (chg : Series or DataFrame) Percentage change between the current and a prior element.</>
  • pipe(func, *args, **kwargs) (object : the return type of ``func``.) Apply func(self, *args, **kwargs).</>
  • pivot(index, columns, values) (DataFrame) Return reshaped DataFrame organized by given index / column values.</>
  • pivot_table(values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort) (DataFrame) Create a spreadsheet-style pivot table as a DataFrame.</>
  • pop(item) (Series) Return item and drop from frame. Raise KeyError if not found.</>
  • prod(axis, skipna, level, numeric_only, min_count, **kwargs) (Series or DataFrame (if level specified)) Return the product of the values over the requested axis.</>
  • quantile(q, axis, numeric_only, interpolation) (Series or DataFrame) Return values at the given quantile over requested axis.</>
  • query(expr, inplace, **kwargs) (DataFrame or None) Query the columns of a DataFrame with a boolean expression.</>
  • rank(axis, method, numeric_only, na_option, ascending, pct) (same type as caller) Compute numerical data ranks (1 through n) along axis.</>
  • reindex_like(other, method, copy, limit, tolerance) (Series or DataFrame) Return an object with matching indices as other object.</>
  • rename(mapper, index, columns, axis, copy, inplace, level, errors) (DataFrame or None) Alter axes labels.</>
  • rename_axis(mapper, index, columns, axis, copy, inplace) (Series, DataFrame, or None) Set the name of the axis for the index or columns.</>
  • reorder_levels(order, axis) (DataFrame) Rearrange index levels using input order. May not drop or duplicate levels.</>
  • replace(to_replace, value, inplace, limit, regex, method) (DataFrame) Replace values given in to_replace with value.</>
  • resample(rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset) (pandas.core.Resampler) Resample time-series data.</>
  • reset_index(level, drop, inplace, col_level, col_fill) (DataFrame or None) Reset the index, or a level of it.</>
  • rolling(window, min_periods, center, win_type, on, axis, closed, method) (a Window or Rolling sub-classed for the particular operation) Provide rolling window calculations.</>
  • round(decimals, *args, **kwargs) (DataFrame) Round a DataFrame to a variable number of decimal places.</>
  • sample(n, frac, replace, weights, random_state, axis, ignore_index) (Series or DataFrame) Return a random sample of items from an axis of object.</>
  • select_dtypes(include, exclude) (DataFrame) Return a subset of the DataFrame's columns based on the column dtypes.</>
  • sem(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased standard error of the mean over requested axis.</>
  • set_axis(labels, axis, inplace) (renamed : DataFrame or None) Assign desired index to given axis.</>
  • set_flags(copy, allows_duplicate_labels) (Series or DataFrame) Return a new object with updated flags.</>
  • set_index(keys, drop, append, inplace, verify_integrity) (DataFrame or None) Set the DataFrame index using existing columns.</>
  • shift(periods, freq, axis, fill_value) (DataFrame) Shift index by desired number of periods with an optional time freq.</>
  • skew(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased skew over requested axis.</>
  • slice_shift(periods, axis) (shifted : same type as caller) Equivalent to shift without copying data.The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. </>
  • sort_index(axis, level, ascending, inplace, kind, na_position, sort_remaining, ignore_index, key) (DataFrame or None) Sort object by labels (along an axis).</>
  • sort_values(by, axis, ascending, inplace, kind, na_position, ignore_index, key) (DataFrame or None) Sort by the values along either axis.</>
  • squeeze(axis) (DataFrame, Series, or scalar) Squeeze 1 dimensional axis objects into scalars.</>
  • stack(level, dropna) (DataFrame or Series) Stack the prescribed level(s) from columns to index.</>
  • std(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return sample standard deviation over requested axis.</>
  • sum(axis, skipna, level, numeric_only, min_count, **kwargs) (Series or DataFrame (if level specified)) Return the sum of the values over the requested axis.</>
  • swapaxes(axis1, axis2, copy) (y : same as input) Interchange axes and swap values axes appropriately.</>
  • swaplevel(i, j, axis) (DataFrame) Swap levels i and j in a :class:MultiIndex.</>
  • tail(n) (type of caller) Return the last n rows.</>
  • take(indices, axis, is_copy, **kwargs) (taken : same type as caller) Return the elements in the given positional indices along an axis.</>
  • to_clipboard(excel, sep, **kwargs) Copy object to the system clipboard.</>
  • to_csv(path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options) (None or str) Write object to a comma-separated values (csv) file.</>
  • to_dict(orient, into) (dict, list or collections.abc.Mapping) Convert the DataFrame to a dictionary.</>
  • to_excel(excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes, storage_options) Write object to an Excel sheet.</>
  • to_feather(path, **kwargs) Write a DataFrame to the binary Feather format.</>
  • to_gbq(destination_table, project_id, chunksize, reauth, if_exists, auth_local_webserver, table_schema, location, progress_bar, credentials) Write a DataFrame to a Google BigQuery table.</>
  • to_hdf(path_or_buf, key, mode, complevel, complib, append, format, index, min_itemsize, nan_rep, dropna, data_columns, errors, encoding) Write the contained data to an HDF5 file using HDFStore.</>
  • to_html(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, max_cols, show_dimensions, decimal, bold_rows, classes, escape, notebook, border, table_id, render_links, encoding) (str or None) Render a DataFrame as an HTML table.</>
  • to_json(path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options) (None or str) Convert the object to a JSON string.</>
  • to_latex(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, bold_rows, column_format, longtable, escape, encoding, decimal, multicolumn, multicolumn_format, multirow, caption, label, position) Render object to a LaTeX tabular, longtable, or nested table/tabular.</>
  • to_markdown(buf, mode, index, storage_options, **kwargs) (str) Print DataFrame in Markdown-friendly format.</>
  • to_numpy(dtype, copy, na_value) (numpy.ndarray) Convert the DataFrame to a NumPy array.</>
  • to_parquet(path, engine, compression, index, partition_cols, storage_options, **kwargs) (bytes if no path argument is provided else None) Write a DataFrame to the binary parquet format.</>
  • to_period(freq, axis, copy) (DataFrame with PeriodIndex) Convert DataFrame from DatetimeIndex to PeriodIndex.</>
  • to_pickle(path, compression, protocol, storage_options) Pickle (serialize) object to file.</>
  • to_records(index, column_dtypes, index_dtypes) (numpy.recarray) Convert DataFrame to a NumPy record array.</>
  • to_sql(name, con, schema, if_exists, index, index_label, chunksize, dtype, method) Write records stored in a DataFrame to a SQL database.</>
  • to_stata(path, convert_dates, write_index, byteorder, time_stamp, data_label, variable_labels, version, convert_strl, compression, storage_options) Export DataFrame object to Stata dta format.</>
  • to_string(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, min_rows, max_cols, show_dimensions, decimal, line_width, max_colwidth, encoding) (str or None) Render a DataFrame to a console-friendly tabular output.</>
  • to_timestamp(freq, how, axis, copy) (DataFrame with DatetimeIndex) Cast to DatetimeIndex of timestamps, at beginning of period.</>
  • to_xarray() (xarray.DataArray or xarray.Dataset) Return an xarray object from the pandas object.</>
  • to_xml(path_or_buffer, index, root_name, row_name, na_rep, attr_cols, elem_cols, namespaces, prefix, encoding, xml_declaration, pretty_print, parser, stylesheet, compression, storage_options) (None or str) Render a DataFrame to an XML document.</>
  • transform(func, axis, *args, **kwargs) (DataFrame) Call func on self producing a DataFrame with transformed values.</>
  • transpose(*args, copy) (DataFrame) Transpose index and columns.</>
  • truncate(before, after, axis, copy) (type of caller) Truncate a Series or DataFrame before and after some index value.</>
  • tshift(periods, freq, axis) (shifted : Series/DataFrame) Shift the time index, using the index's frequency if available.</>
  • tz_convert(tz, axis, level, copy) ({klass}) Convert tz-aware axis to target time zone.</>
  • tz_localize(tz, axis, level, copy, ambiguous, nonexistent) (Series or DataFrame) Localize tz-naive index of a Series or DataFrame to target time zone.</>
  • unstack(level, fill_value) (Series or DataFrame) Pivot a level of the (necessarily hierarchical) index labels.</>
  • update(other, join, overwrite, filter_func, errors) (None : method directly changes calling object) Modify in place using non-NA values from another DataFrame.</>
  • value_counts(subset, normalize, sort, ascending, dropna) (Series) Return a Series containing counts of unique rows in the DataFrame.</>
  • var(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased variance over requested axis.</>
  • xs(key, axis, level, drop_level) (Series or DataFrame) Return cross-section from the Series/DataFrame.</>
method

__dir__() → list

Provide method name lookup and completion.

Notes

Only provide 'public' methods.

method

__sizeof__() → int

Generates the total memory usage for an object that returnseither a value or Series of values

method

set_flags(copy=False, allows_duplicate_labels=None)

Return a new object with updated flags.

Parameters
  • allows_duplicate_labels (bool, optional) Whether the returned object allows duplicate labels.
Returns (Series or DataFrame)

The same type as the caller.

See Also

DataFrame.attrs : Global metadata applying to this dataset.DataFrame.flags : Global flags applying to this object.

Notes

This method returns a new object that's a view on the same data as the input. Mutating the input or the output values will be reflected in the other.

This method is intended to be used in method chains.

"Flags" differ from "metadata". Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in :attr:DataFrame.attrs.

Examples
>>> df = pd.DataFrame({"A": [1, 2]})>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False
method

swapaxes(axis1, axis2, copy=True)

Interchange axes and swap values axes appropriately.

method

droplevel(level, axis=0)

Return Series/DataFrame with requested index / column level(s) removed.

Parameters
  • level (int, str, or list-like) If a string is given, must be the name of a levelIf list-like, elements must be names or positional indexes of levels.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis along which the level(s) is removed:
    • 0 or 'index': remove level(s) in column.
    • 1 or 'columns': remove level(s) in row.
Returns (Series/DataFrame)

Series/DataFrame with requested index / column level(s) removed.

Examples
>>> df = pd.DataFrame([...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
...     ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1   c   d
level_2   e   f
a b
1 2      3   4
5 6      7   8
9 10    11  12
>>> df.droplevel('a')
level_1   c   d
level_2   e   f
b
2        3   4
6        7   8
10      11  12
>>> df.droplevel('level_2', axis=1)
level_1   c   d
a b
1 2      3   4
5 6      7   8
9 10    11  12
method

squeeze(axis=None)

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don't know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default None) A specific axis to squeeze. By default, all length-1 axes aresqueezed.
Returns (DataFrame, Series, or scalar)

The projection after squeezing axis or all the axes.

See Also

Series.iloc : Integer-location based indexing for selecting scalars.DataFrame.iloc : Integer-location based indexing for selecting Series. Series.to_frame : Inverse of DataFrame.squeeze for a single-column DataFrame.

Examples
>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0    2
dtype: int64
>>> even_primes.squeeze()
2

Squeezing objects with more than one value in every axis does nothing:

>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1    3
2    5
3    7
dtype: int64
>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

>>> df_a = df[['a']]
>>> df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

>>> df_a.squeeze('columns')
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

>>> df_0a.squeeze('rows')
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_0a.squeeze()
1
method

rename_axis(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False)

Set the name of the axis for the index or columns.

Parameters
  • mapper (scalar, list-like, optional) Value to set the axis name attribute.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to rename.
  • copy (bool, default True) Also copy underlying data.
  • inplace (bool, default False) Modifies the object directly, instead of creating a new Seriesor DataFrame.
Returns (Series, DataFrame, or None)

The same type as the caller or None if inplace=True.

See Also

Series.rename : Alter Series index labels or name.DataFrame.rename : Alter DataFrame index labels or name. Index.rename : Set new names on index.

Notes

DataFrame.rename_axis supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)
  • (mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

Series

>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0       dog
1       cat
2    monkey
dtype: object
>>> s.rename_axis("animal")
animal
0    dog
1    cat
2    monkey
dtype: object

DataFrame

>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
...                    "num_arms": [0, 0, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product([['mammal'],
...                                        ['dog', 'cat', 'monkey']],
...                                       names=['type', 'name'])
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(index={'type': 'class'})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
method

equals(other)

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype.

Parameters
  • other (Series or DataFrame) The other Series or DataFrame to be compared with the first.
Returns (bool)

True if all elements are the same in both objects, Falseotherwise.

See Also

Series.eq : Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise. DataFrame.eq : Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise. testing.assert_series_equal : Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others. testing.assert_frame_equal : Like assert_series_equal, but targets DataFrames. numpy.array_equal : Return True if two arrays have the same shape and elements, False otherwise.

Examples
>>> df = pd.DataFrame({1: [10], 2: [20]})>>> df
    1   2
0  10  20

DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.

>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.

>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
      1     2
0  10.0  20.0
>>> df.equals(different_data_type)
False
method

bool()

Return the bool of a single element Series or DataFrame.

This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).

Returns (bool)

The value in the Series or DataFrame.

See Also

Series.astype : Change the data type of a Series, including to boolean.DataFrame.astype : Change the data type of a DataFrame, including to boolean. numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.

Examples

The method will only work for single element objects with a boolean value:

>>> pd.Series([True]).bool()
True
>>> pd.Series([False]).bool()
False
>>> pd.DataFrame({'col': [True]}).bool()
True
>>> pd.DataFrame({'col': [False]}).bool()
False
method

__iter__()

Iterate over info axis.

Returns (iterator)

Info axis as iterator.

method

keys()

Get the 'info axis' (see Indexing for more).

This is index for Series, columns for DataFrame.

Returns (Index)

Info axis.

method

__contains__(key) → bool

True if the key is in the info axis

method

__array_wrap__(result, context=None)

Gets called after a ufunc and other functions.

Parameters
  • result (np.ndarray) The result of the ufunc or other function called on the NumPy arrayreturned by array
  • context (tuple of (func, tuple, int)) This parameter is returned by ufuncs as a 3-element tuple: (name of theufunc, arguments of the ufunc, domain of the ufunc), but is not set by other numpy functions.q

Notes

Series implements __array_ufunc_ so this not called for ufunc on Series.

method

to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None, storage_options=None)

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters
  • excel_writer (path-like, file-like, or ExcelWriter object) File path or existing ExcelWriter.
  • sheet_name (str, default 'Sheet1') Name of sheet which will contain DataFrame.
  • na_rep (str, default '') Missing data representation.
  • float_format (str, optional) Format string for floating point numbers. For examplefloat_format="%.2f" will format 0.1234 to 0.12.
  • columns (sequence or list of str, optional) Columns to write.
  • header (bool or list of str, default True) Write out the column names. If a list of string is given it isassumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • index_label (str or sequence, optional) Column label for index column(s) if desired. If not specified, andheader and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
  • startrow (int, default 0) Upper left cell row to dump data frame.
  • startcol (int, default 0) Upper left cell column to dump data frame.
  • engine (str, optional) Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set thisvia the options io.excel.xlsx.writer, io.excel.xls.writer, and io.excel.xlsm.writer.
    .. deprecated:: 1.2.0
    As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
    maintained, the ``xlwt`` engine will be removed in a future version
    of pandas.
    
  • merge_cells (bool, default True) Write MultiIndex and Hierarchical Rows as merged cells.
  • encoding (str, optional) Encoding of the resulting excel file. Only necessary for xlwt,other writers support unicode natively.
  • inf_rep (str, default 'inf') Representation for infinity (there is no native representation forinfinity in Excel).
  • verbose (bool, default True) Display more information in the error logs.
  • freeze_panes (tuple of int (length 2), optional) Specifies the one-based bottommost row and rightmost column thatis to be frozen.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
See Also

to_csv : Write DataFrame to a comma-separated values (csv) file.ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame.

Notes

For compatibility with :meth:~DataFrame.to_csv, to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.

Examples

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method

to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=True, indent=None, storage_options=None)

Convert the object to a JSON string.

Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters
  • path_or_buf (str or file handle, optional) File path or object. If not specified, the result is returned asa string.
  • orient (str) Indication of expected JSON string format.
    • Series:
      • default is 'index'
      • allowed values are: {'split', 'records', 'index', 'table'}.
    • DataFrame:
      • default is 'columns'
      • allowed values are: {'split', 'records', 'index', 'columns', 'values', 'table'}.
    • The format of the JSON string:
      • 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
      • 'records' : list like [{column -> value}, ... , {column -> value}]
      • 'index' : dict like {index -> {column -> value}}
      • 'columns' : dict like {column -> {index -> value}}
      • 'values' : just the values array
      • 'table' : dict like {'schema': {schema}, 'data': {data}}
      Describing the data, where data component is like orient='records'.
  • date_format ({None, 'epoch', 'iso'}) Type of date conversion. 'epoch' = epoch milliseconds,'iso' = ISO8601. The default depends on the orient. For orient='table', the default is 'iso'. For all other orients, the default is 'epoch'.
  • double_precision (int, default 10) The number of decimal places to use when encodingfloating point values.
  • force_ascii (bool, default True) Force encoded string to be ASCII.
  • date_unit (str, default 'ms' (milliseconds)) The time unit to encode to, governs timestamp and ISO8601precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively.
  • default_handler (callable, default None) Handler to call if object cannot otherwise be converted to asuitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
  • lines (bool, default False) If 'orient' is 'records' write out line-delimited json format. Willthrow ValueError if incorrect 'orient' since others are not list-like.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}) ,e .
  • index (bool, default True) Whether to include the index values in the JSON string. Notincluding the index (index=False) is only supported when orient is 'split' or 'table'.
  • indent (int, optional) Length of whitespace used to indent each record.
    .. versionadded:: 1.0.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
Returns (None or str)

If path_or_buf is None, returns the resulting json format as astring. Otherwise returns None.

See Also

read_json : Convert a JSON string to pandas object.

Notes

The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release.

orient='table' contains a 'pandas_version' field under 'schema'. This stores the version of pandas used in the latest revision of the schema.

Examples
>>> import json>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> result = df.to_json(orient="records")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> result = df.to_json(orient="index")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "row 1": {
        "col 1": "a",
        "col 2": "b"
    },
    "row 2": {
        "col 1": "c",
        "col 2": "d"
    }
}

Encoding/decoding a Dataframe using 'columns' formatted JSON:

>>> result = df.to_json(orient="columns")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "col 1": {
        "row 1": "a",
        "row 2": "c"
    },
    "col 2": {
        "row 1": "b",
        "row 2": "d"
    }
}

Encoding/decoding a Dataframe using 'values' formatted JSON:

>>> result = df.to_json(orient="values")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
[
    [
        "a",
        "b"
    ],
    [
        "c",
        "d"
    ]
]

Encoding with Table Schema:

>>> result = df.to_json(orient="table")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "schema": {
        "fields": [
            {
                "name": "index",
                "type": "string"
            },
            {
                "name": "col 1",
                "type": "string"
            },
            {
                "name": "col 2",
                "type": "string"
            }
        ],
        "primaryKey": [
            "index"
        ],
        "pandas_version": "0.20.0"
    },
    "data": [
        {
            "index": "row 1",
            "col 1": "a",
            "col 2": "b"
        },
        {
            "index": "row 2",
            "col 1": "c",
            "col 2": "d"
        }
    ]
}
method

to_hdf(path_or_buf, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

.. warning::

One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.

For more information see the :ref:user guide <io.hdf5>.

Parameters
  • path_or_buf (str or pandas.HDFStore) File path or HDFStore object.
  • key (str) Identifier for the group in the store.
  • mode ({'a', 'w', 'r+'}, default 'a') Mode to open file:
    • 'w': write, a new file is created (an existing file with the same name would be deleted).
    • 'a': append, an existing file is opened for reading and writing, and if the file does not exist it is created.
    • 'r+': similar to 'a', but the file must already exist.
  • complevel ({0-9}, optional) Specifies a compression level for data.A value of 0 disables compression.
  • complib ({'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib') Specifies the compression library to be used.As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError.
  • append (bool, default False) For Table formats, append the input data to the existing.
  • format ({'fixed', 'table', None}, default 'fixed') Possible values:
    • 'fixed': Fixed format. Fast writing/reading. Not-appendable, nor searchable.
    • 'table': Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
    • If None, pd.get_option('io.hdf.default_format') is checked, followed by fallback to "fixed"
  • min_itemsize (dict or int, optional) Map column names to minimum string sizes for columns.
  • nan_rep (Any, optional) How to represent null values as str.Not allowed with append=True.
  • data_columns (list of columns or True, optional) List of columns to create as indexed data columns for on-diskqueries, or True to use all columns. By default only the axes of the object are indexed. See :ref:io.hdf5-query-data-columns. Applicable only to format='table'.
  • errors (str, default 'strict') Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:open for a full list of options.
See Also

read_hdf : Read from HDF file.DataFrame.to_parquet : Write a DataFrame to the binary parquet format. DataFrame.to_sql : Write to a SQL table. DataFrame.to_feather : Write out feather-format for DataFrames. DataFrame.to_csv : Write out to a csv file.

Examples
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},...                   index=['a', 'b', 'c'])
>>> df.to_hdf('data.h5', key='df', mode='w')

We can add another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf('data.h5', key='s')

Reading from HDF file:

>>> pd.read_hdf('data.h5', 'df')
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')
0    1
1    2
2    3
3    4
dtype: int64

Deleting file with data:

>>> import os
>>> os.remove('data.h5')
method

to_sql(name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.

Parameters
  • name (str) Name of SQL table.
  • con (sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection) Using SQLAlchemy makes it possible to use any DB supported by thatlibrary. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See here <https://docs.sqlalchemy.org/en/13/core/connections.html>_.
  • schema (str, optional) Specify the schema (if database flavor supports this). If None, usedefault schema.
  • if_exists ({'fail', 'replace', 'append'}, default 'fail') How to behave if the table already exists.
    • fail: Raise a ValueError.
    • replace: Drop the table before inserting new values.
    • append: Insert new values to the existing table.
  • index (bool, default True) Write DataFrame index as a column. Uses index_label as the columnname in the table.
  • index_label (str or sequence, default None) Column label for index column(s). If None is given (default) andindex is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
  • chunksize (int, optional) Specify the number of rows in each batch to be written at a time.By default, all rows will be written at once.
  • dtype (dict or scalar, optional) Specifying the datatype for columns. If a dictionary is used, thekeys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.
  • method ({None, 'multi', callable}, optional) Controls the SQL insertion clause used:
    • None : Uses standard SQL INSERT clause (one per row).
    • 'multi': Pass multiple values in a single INSERT clause.
    • callable with signature (pd_table, conn, keys, data_iter).
    Details and a sample callable implementation can be found in the section :ref:insert method <io.sql.method>.
Raises
  • ValueError When the table already exists and if_exists is 'fail' (thedefault).
See Also

read_sql : Read a DataFrame from a table.

Notes

Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

References

.. [1] https://docs.sqlalchemy.org.. [2] https://www.python.org/dev/peps/pep-0249/

Examples

Create an in-memory SQLite database.

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)

Create a table from scratch with 3 rows.

>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
     name
0  User 1
1  User 2
2  User 3
>>> df.to_sql('users', con=engine)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

An sqlalchemy.engine.Connection can also be passed to con:

>>> with engine.begin() as connection:
...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
...     df1.to_sql('users', con=connection, if_exists='append')

This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql('users', con=engine, if_exists='append')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
 (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
 (1, 'User 7')]

Overwrite the table with just df2.

>>> df2.to_sql('users', con=engine, if_exists='replace',
...            index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 6'), (1, 'User 7')]

Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
     A
0  1.0
1  NaN
2  2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql('integers', con=engine, index=False,
...           dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall()
[(1,), (None,), (2,)]
method

to_pickle(path, compression='infer', protocol=5, storage_options=None)

Pickle (serialize) object to file.

Parameters
  • path (str) File path where the pickled object will be stored.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') A string representing the compression to use in the output file. Bydefault, infers from the file extension in specified path. Compression mode may be any of the following possible values: {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and path_or_buf is path-like, then detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression). If dict given and mode is ‘zip’ or inferred as ‘zip’, other entries passed as additional compression options.
  • protocol (int) Int which indicates which protocol should be used by the pickler,default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
    .. [1] https://docs.python.org/3/library/pickle.html.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
See Also

read_pickle : Load pickled pandas object (or any object) from file.DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Examples
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> import os
>>> os.remove("./dummy.pkl")
method

to_clipboard(excel=True, sep=None, **kwargs)

Copy object to the system clipboard.

Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.

Parameters
  • excel (bool, default True) Produce output in a csv format for easy pasting into excel.
    • True, use the provided separator for csv pasting.
    • False, write a string representation of the object to the clipboard.
  • sep (str, default ``'\t'``) Field delimiter.
  • **kwargs These parameters will be passed to DataFrame.to_csv.
See Also

DataFrame.to_csv : Write a DataFrame to a comma-separated values (csv) file. read_clipboard : Read text from clipboard and pass to read_table.

Notes

Requirements for your platform.

  • Linux : xclip, or xsel (with PyQt4 modules)
  • Windows : none
  • OS X : none
Examples

Copy the contents of a DataFrame to the clipboard.

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',')  # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6

We can omit the index by passing the keyword index and setting it to false.

>>> df.to_clipboard(sep=',', index=False)  # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
method

to_xarray()

Return an xarray object from the pandas object.

Returns (xarray.DataArray or xarray.Dataset)

Data in the pandas structure converted to Dataset if the object isa DataFrame, or a DataArray if the object is a Series.

See Also

DataFrame.to_hdf : Write DataFrame to an HDF5 file.DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Notes

See the xarray docs <https://xarray.pydata.org/en/stable/>__

Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),...                    ('parrot', 'bird', 24.0, 2),
...                    ('lion', 'mammal', 80.5, 4),
...                    ('monkey', 'mammal', np.nan, 4)],
...                   columns=['name', 'class', 'max_speed',
...                            'num_legs'])
>>> df
     name   class  max_speed  num_legs
0  falcon    bird      389.0         2
1  parrot    bird       24.0         2
2    lion  mammal       80.5         4
3  monkey  mammal        NaN         4
>>> df.to_xarray()
<xarray.Dataset>
Dimensions:    (index: 4)
Coordinates:
  * index      (index) int64 0 1 2 3
Data variables:
    name       (index) object 'falcon' 'parrot' 'lion' 'monkey'
    class      (index) object 'bird' 'bird' 'mammal' 'mammal'
    max_speed  (index) float64 389.0 24.0 80.5 nan
    num_legs   (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. ,  24. ,  80.5,   nan])
Coordinates:
  * index    (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
...                         '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
...                               'animal': ['falcon', 'parrot',
...                                          'falcon', 'parrot'],
...                               'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
                   speed
date       animal
2018-01-01 falcon    350
           parrot     18
2018-01-02 falcon    361
           parrot     15
>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions:  (animal: 2, date: 2)
Coordinates:
  * date     (date) datetime64[ns] 2018-01-01 2018-01-02
  * animal   (animal) object 'falcon' 'parrot'
Data variables:
    speed    (date, animal) int64 350 18 361 15
method

to_latex(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, position=None)

Render object to a LaTeX tabular, longtable, or nested table/tabular.

Requires \usepackage{booktabs}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{table.tex}.

.. versionchanged:: 1.0.0 Added caption and label arguments.

.. versionchanged:: 1.2.0 Added position argument, changed meaning of caption argument.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (list of label, optional) The subset of columns to write. Writes all columns by default.
  • col_space (int, optional) The minimum width of each column.
  • header (bool or list of str, default True) Write out the column names. If a list of strings is given,it is assumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • na_rep (str, default 'NaN') Missing data representation.
  • formatters (list of functions or dict of {str: function}, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List must be of length equal to the number of columns.
  • float_format (one-parameter function or str, optional, default None) Formatter for floating point numbers. For examplefloat_format="%.2f" and float_format="{:0.2f}".format will both result in 0.1234 being formatted as 0.12.
  • sparsify (bool, optional) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row. By default, the value will be read from the config module.
  • index_names (bool, default True) Prints the names of the indexes.
  • bold_rows (bool, default False) Make the row labels bold in the output.
  • column_format (str, optional) The columns format as specified in LaTeX table format<https://en.wikibooks.org/wiki/LaTeX/Tables>__ e.g. 'rcl' for 3 columns. By default, 'l' will be used for all columns except columns of numbers, which default to 'r'.
  • longtable (bool, optional) By default, the value will be read from the pandas configmodule. Use a longtable environment instead of tabular. Requires adding a \usepackage{longtable} to your LaTeX preamble.
  • escape (bool, optional) By default, the value will be read from the pandas configmodule. When set to False prevents from escaping latex special characters in column names.
  • encoding (str, optional) A string representing the encoding to use in the output file,defaults to 'utf-8'.
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • multicolumn (bool, default True) Use \multicolumn to enhance MultiIndex columns.The default will be read from the config module.
  • multicolumn_format (str, default 'l') The alignment for multicolumns, similar to column_formatThe default will be read from the config module.
  • multirow (bool, default False) Use \multirow to enhance MultiIndex rows. Requires adding a\usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module.
  • caption (str or tuple, optional) Tuple (full_caption, short_caption),which results in \caption[short_caption]{full_caption}; if a single string is passed, no short caption will be set.
    .. versionadded:: 1.0.0
    .. versionchanged:: 1.2.0 Optionally allow caption to be a tuple (full_caption, short_caption).
  • label (str, optional) The LaTeX label to be placed inside \label{} in the output.This is used with \ref{} in the main .tex file.
    .. versionadded:: 1.0.0
  • position (str, optional) The LaTeX positional argument for tables, to be placed after\begin{} in the output.
    .. versionadded:: 1.2.0
    Returns
    -------
    str or None
        If buf is None, returns the result as a string. Otherwise returns
        None.
    
See Also

DataFrame.to_string : Render a DataFrame to a console-friendly tabular output. DataFrame.to_html : Render a DataFrame as an HTML table.

Examples
>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],...                   mask=['red', 'purple'],
...                   weapon=['sai', 'bo staff']))
>>> print(df.to_latex(index=False))  # doctest: +NORMALIZE_WHITESPACE
\begin{tabular}{lll}
 \toprule
       name &    mask &    weapon \\
 \midrule
    Raphael &     red &       sai \\
  Donatello &  purple &  bo staff \\
\bottomrule
\end{tabular}
method

to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)

Write object to a comma-separated values (csv) file.

Parameters
  • path_or_buf (str or file handle, default None) File path or object, if None is provided the result is returned asa string. If a non-binary file object is passed, it should be opened with newline='', disabling universal newlines. If a binary file object is passed, mode might need to contain a 'b'.
    .. versionchanged:: 1.2.0
    Support for binary file objects was introduced.
  • sep (str, default ',') String of length 1. Field delimiter for the output file.
  • na_rep (str, default '') Missing data representation.
  • float_format (str, default None) Format string for floating point numbers.
  • columns (sequence, optional) Columns to write.
  • header (bool or list of str, default True) Write out the column names. If a list of strings is given it isassumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • index_label (str or sequence, or False, default None) Column label for index column(s) if desired. If None is given, andheader and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.
  • mode (str) Python write mode, default 'w'.
  • encoding (str, optional) A string representing the encoding to use in the output file,defaults to 'utf-8'. encoding is not supported if path_or_buf is a non-binary file object.
  • compression (str or dict, default 'infer') If str, represents compression mode. If dict, value at 'method' isthe compression mode. Compression mode may be any of the following possible values: {'infer', 'gzip', 'bz2', 'zip', 'xz', None}. If compression mode is 'infer' and path_or_buf is path-like, then detect compression mode from the following extensions: '.gz', '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given and mode is one of {'zip', 'gzip', 'bz2'}, or inferred as one of the above, other entries passed as additional compression options.
    .. versionchanged:: 1.0.0
    May now be a dict with key 'method' as compression mode and other entries as additional compression options if compression mode is 'zip'.
    .. versionchanged:: 1.1.0
    Passing compression options as keys in dict is supported for compression modes 'gzip' and 'bz2' as well as 'zip'.
    .. versionchanged:: 1.2.0
    Compression is supported for binary file objects.
    
    .. versionchanged:: 1.2.0
    Previous versions forwarded dict entries for 'gzip' to
    `gzip.open` instead of `gzip.GzipFile` which prevented
    setting `mtime`.
    
  • quoting (optional constant from csv module) Defaults to csv.QUOTE_MINIMAL. If you have set a float_formatthen floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.
  • quotechar (str, default '\"') String of length 1. Character used to quote fields.
  • line_terminator (str, optional) The newline character or character sequence to use in the outputfile. Defaults to os.linesep, which depends on the OS in which this method is called ('\n' for linux, '\r\n' for Windows, i.e.).
  • chunksize (int or None) Rows to write at a time.
  • date_format (str, default None) Format string for datetime objects.
  • doublequote (bool, default True) Control quoting of quotechar inside a field.
  • escapechar (str, default None) String of length 1. Character used to escape sep and quotecharwhen appropriate.
  • decimal (str, default '.') Character recognized as decimal separator. E.g. use ',' forEuropean data.
  • errors (str, default 'strict') Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:open for a full list of options.
    .. versionadded:: 1.1.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
Returns (None or str)

If path_or_buf is None, returns the resulting csv format as astring. Otherwise returns None.

See Also

read_csv : Load a CSV file into a DataFrame.to_excel : Write DataFrame to an Excel file.

Examples
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'

Create 'out.zip' containing 'out.csv'

>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  # doctest: +SKIP
method

take(indices, axis=0, is_copy=None, **kwargs)

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

Parameters
  • indices (array-like) An array of ints indicating which positions to take.
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) The axis on which to select elements. 0 means that we areselecting rows, 1 means that we are selecting columns.
  • is_copy (bool) Before pandas 1.0, is_copy=False can be specified to ensurethat the return value is an actual copy. Starting with pandas 1.0, take always returns a copy, and the keyword is therefore deprecated.
    .. deprecated:: 1.0.0
  • **kwargs For compatibility with :meth:numpy.take. Has no effect on theoutput.
Returns (taken : same type as caller)

An array-like containing the elements taken from the object.

See Also

DataFrame.loc : Select a subset of a DataFrame by labels.DataFrame.iloc : Select a subset of a DataFrame by positions. numpy.take : Take elements from an array along an axis.

Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=['name', 'class', 'max_speed'],
...                   index=[0, 2, 3, 1])
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That's because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
     name   class  max_speed
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5
method

xs(key, axis=0, level=None, drop_level=True)

Return cross-section from the Series/DataFrame.

This method takes a key argument to select data at a particular level of a MultiIndex.

Parameters
  • key (label or tuple of label) Label contained in the index, or partially in a MultiIndex.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis to retrieve cross-section on.
  • level (object, defaults to first n levels (n=1 or len(key))) In case of a key partially contained in a MultiIndex, indicatewhich levels are used. Levels can be referred by label or position.
  • drop_level (bool, default True) If False, returns object with same levels as self.
Returns (Series or DataFrame)

Cross-section from the original Series or DataFramecorresponding to the selected index levels.

See Also

DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc : Purely integer-location based indexing for selection by position.

Notes

xs can not be used to set values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see :ref:MultiIndex Slicers <advanced.mi_slicers>.

Examples
>>> d = {'num_legs': [4, 4, 2, 2],...      'num_wings': [0, 0, 2, 2],
...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
...      'animal': ['cat', 'dog', 'bat', 'penguin'],
...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
                           num_legs  num_wings
class  animal  locomotion
mammal cat     walks              4          0
       dog     walks              4          0
       bat     flies              2          2
bird   penguin walks              2          2

Get values at specified index

>>> df.xs('mammal')
                   num_legs  num_wings
animal locomotion
cat    walks              4          0
dog    walks              4          0
bat    flies              2          2

Get values at several indexes

>>> df.xs(('mammal', 'dog'))
            num_legs  num_wings
locomotion
walks              4          0

Get values at specified index and level

>>> df.xs('cat', level=1)
                   num_legs  num_wings
class  locomotion
mammal walks              4          0

Get values at several indexes and levels

>>> df.xs(('bird', 'walks'),
...       level=[0, 'locomotion'])
         num_legs  num_wings
animal
penguin         2          2

Get values at specified column and axis

>>> df.xs('num_wings', axis=1)
class   animal   locomotion
mammal  cat      walks         0
        dog      walks         0
        bat      flies         2
bird    penguin  walks         2
Name: num_wings, dtype: int64
method

__delitem__(key)

Delete item

method

get(key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

method

reindex_like(other, method=None, copy=True, limit=None, tolerance=None)

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters
  • other (Object of the same data type) Its row and column indices are used to define the new indicesof this object.
  • method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) Method to use for filling holes in reindexed DataFrame.Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.
    • None (default): don't fill gaps
    • pad / ffill: propagate last valid observation forward to next valid
    • backfill / bfill: use next valid observation to fill gap
    • nearest: use nearest valid observations to fill gap.
  • copy (bool, default True) Return a new object, even if the passed indexes are the same.
  • limit (int, default None) Maximum number of consecutive labels to fill for inexact matches.
  • tolerance (optional) Maximum distance between original and new labels for inexactmatches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.
    Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type.
Returns (Series or DataFrame)

Same type as caller, but with changed indices on each axis.

See Also

DataFrame.set_index : Set row labels.DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...).

Examples
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],...                     [31, 87.8, 'high'],
...                     [22, 71.6, 'medium'],
...                     [35, 95, 'medium']],
...                    columns=['temp_celsius', 'temp_fahrenheit',
...                             'windspeed'],
...                    index=pd.date_range(start='2014-02-12',
...                                        end='2014-02-15', freq='D'))
>>> df1
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium
>>> df2 = pd.DataFrame([[28, 'low'],
...                     [30, 'low'],
...                     [35.1, 'medium']],
...                    columns=['temp_celsius', 'windspeed'],
...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
...                                            '2014-02-15']))
>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium
>>> df2.reindex_like(df1)
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       NaN
2014-02-15          35.1              NaN    medium
method

add_prefix(prefix)

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters
  • prefix (str) The string to add before each label.
Returns (Series or DataFrame)

New Series or DataFrame with updated labels.

See Also

Series.add_suffix: Suffix row labels with string suffix.DataFrame.add_suffix: Suffix column labels with string suffix.

Examples
>>> s = pd.Series([1, 2, 3, 4])>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_prefix('item_')
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_prefix('col_')
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6
method

add_suffix(suffix)

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters
  • suffix (str) The string to add after each label.
Returns (Series or DataFrame)

New Series or DataFrame with updated labels.

See Also

Series.add_prefix: Prefix row labels with string prefix.DataFrame.add_prefix: Prefix column labels with string prefix.

Examples
>>> s = pd.Series([1, 2, 3, 4])>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_suffix('_item')
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_suffix('_col')
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6
method

filter(items=None, like=None, regex=None, axis=None)

Subset the dataframe rows or columns according to the specified index labels.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Parameters
  • items (list-like) Keep labels from axis which are in items.
  • like (str) Keep labels from axis for which "like in label == True".
  • regex (str (regular expression)) Keep labels from axis for which re.search(regex, label) == True.
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) The axis to filter on, expressed either as an index (int)or axis name (str). By default this is the info axis, 'index' for Series, 'columns' for DataFrame.
See Also

DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array.

Notes

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

Examples
>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])
>>> df
        one  two  three
mouse     1    2      3
rabbit    4    5      6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
         one  three
mouse     1      3
rabbit    4      6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6
method

head(n=5)

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last n rows, equivalent to df[:-n].

Parameters
  • n (int, default 5) Number of rows to select.
Returns (same type as caller)

The first n rows of the caller object.

See Also

DataFrame.tail: Returns the last n rows.

Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the first 5 lines

>>> df.head()
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey

Viewing the first n lines (three in this case)

>>> df.head(3)
      animal
0  alligator
1        bee
2     falcon

For negative values of n

>>> df.head(-3)
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
method

tail(n=5)

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first n rows, equivalent to df[n:].

Parameters
  • n (int, default 5) Number of rows to select.
Returns (type of caller)

The last n rows of the caller object.

See Also

DataFrame.head : The first n rows of the caller object.

Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the last 5 lines

>>> df.tail()
   animal
4  monkey
5  parrot
6   shark
7   whale
8   zebra

Viewing the last n lines (three in this case)

>>> df.tail(3)
  animal
6  shark
7  whale
8  zebra

For negative values of n

>>> df.tail(-3)
   animal
3    lion
4  monkey
5  parrot
6   shark
7   whale
8   zebra
method

sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters
  • n (int, optional) Number of items from axis to return. Cannot be used with frac.Default = 1 if frac = None.
  • frac (float, optional) Fraction of axis items to return. Cannot be used with n.
  • replace (bool, default False) Allow or disallow sampling of the same row more than once.
  • weights (str or ndarray-like, optional) Default 'None' results in equal probability weighting.If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.
  • random_state (int, array-like, BitGenerator, np.random.RandomState, optional) If int, array-like, or BitGenerator (NumPy>=1.17), seed forrandom number generator If np.random.RandomState, use as numpy RandomState object.
    .. versionchanged:: 1.1.0
    array-like and BitGenerator (for NumPy>=1.17) object now passed to
    np.random.RandomState() as seed
    
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) Axis to sample. Accepts axis number or name. Default is stat axisfor given data type (0 for Series and DataFrames).
  • ignore_index (bool, default False) If True, the resulting index will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.3.0
Returns (Series or DataFrame)

A new object of same type as caller containing n items randomlysampled from the caller object.

See Also

DataFrameGroupBy.sample: Generates random samples from each group of a DataFrame object. SeriesGroupBy.sample: Generates random samples from each group of a Series object. numpy.random.choice: Generates a random sample from a given 1-D numpy array.

Notes

If frac > 1, replacement should be set to True.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],...                    'num_wings': [2, 0, 0, 0],
...                    'num_specimen_seen': [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df['num_legs'].sample(n=3, random_state=1)
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1)
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8
method

pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs).

Parameters
  • func (function) Function to apply to the Series/DataFrame.args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
See Also

DataFrame.apply : Apply a function along input axis of DataFrame.DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Series.map : Apply a mapping correspondence on a :class:~pandas.Series.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing

>>> func(g(h(df), arg1=a), arg2=b, arg3=c)  # doctest: +SKIP

You can write

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe(func, arg2=b, arg3=c)
... )  # doctest: +SKIP

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose f takes its data as arg2:

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe((func, 'arg2'), arg1=a, arg3=c)
...  )  # doctest: +SKIP
method

__finalize__(other, method=None, **kwargs) → FrameOrSeries

Propagate metadata from other to self.

Parameters
  • other (the object from which to get the attributes that we are going) to propagate
  • method (str, optional) A passed method name providing context on where __finalize__was called.
    .. warning::
    The value passed as method are not currently considered stable across pandas releases.
method

__getattr__(name)

After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use.

method

__setattr__(name, value)

After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use.

method

astype(dtype, copy=True, errors='raise')

Cast a pandas object to a specified dtype dtype.

Parameters
  • dtype (data type, or dict of column name -> data type) Use a numpy.dtype or Python type to cast entire pandas object tothe same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types.
  • copy (bool, default True) Return a copy when copy=True (be very careful settingcopy=False as changes to values then may propagate to other pandas objects).
  • errors ({'raise', 'ignore'}, default 'raise') Control raising of exceptions on invalid data for provided dtype.
    • raise : allow exceptions to be raised
    • ignore : suppress exceptions. On error return original object.
See Also

to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type.

Notes

.. deprecated:: 1.3.0

Using ``astype`` to convert from timezone-naive dtype to
timezone-aware dtype is deprecated and will raise in a
future version.  Use :meth:`Series.dt.tz_localize` instead.
Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype('int32').dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({'col1': 'int32'}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype('int64')
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')
0    1
1    2
dtype: category
Categories (2, int64): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
...     categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Note that using copy=False and changing data on a new pandas object may propagate changes:

>>> s1 = pd.Series([1, 2])
>>> s2 = s1.astype('int64', copy=False)
>>> s2[0] = 10
>>> s1  # note that s1[0] has changed too
0    10
1     2
dtype: int64

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[ns]
method

__deepcopy__(memo=None) → FrameOrSeries

method

infer_objects()

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

See Also

to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to numeric type. convert_dtypes : Convert argument to best possible dtype.

Examples
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3
>>> df.dtypes
A    object
dtype: object
>>> df.infer_objects().dtypes
A    int64
dtype: object
method

convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True)

Convert columns to best possible dtypes using dtypes supporting pd.NA.

.. versionadded:: 1.0.0

Parameters
  • infer_objects (bool, default True) Whether object dtypes should be converted to the best possible types.
  • convert_string (bool, default True) Whether object dtypes should be converted to StringDtype().
  • convert_integer (bool, default True) Whether, if possible, conversion can be done to integer extension types.
  • convert_boolean (bool, defaults True) Whether object dtypes should be converted to BooleanDtypes().
  • convert_floating (bool, defaults True) Whether, if possible, conversion can be done to floating extension types.If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.
    .. versionadded:: 1.2.0
Returns (Series or DataFrame)

Copy of input object with new dtype.

See Also

infer_objects : Infer dtypes of objects.to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.

.. versionchanged:: 1.2 Starting with pandas 1.2, this method also converts float columns to the nullable floating extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples
>>> df = pd.DataFrame(...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0
>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10   <NA>
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0
>>> dfn.dtypes
a      Int32
b     string
c    boolean
d     string
e      Int64
f    Float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: object

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string
method

ffill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for :meth:DataFrame.fillna with method='ffill'.

Returns (Series/DataFrame or None)

Object with missing values filled or None if inplace=True.

method

bfill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for :meth:DataFrame.fillna with method='bfill'.

Returns (Series/DataFrame or None)

Object with missing values filled or None if inplace=True.

method

asof(where, subset=None)

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a :class:~pandas.DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters
  • where (date or array-like of dates) Date(s) before which the last row(s) are returned.
  • subset (str or array-like of str, default `None`) For DataFrame, if not None, only use these columns tocheck for NaNs.
Returns (scalar, Series, or DataFrame)

:

r , r n e

.

See Also

merge_asof : Perform an asof merge. Similar to left join.

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64
>>> s.asof(20)
2.0

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
2.0

Take all columns into consideration

>>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
...                    'b': [None, None, None, None, 500]},
...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
...                                           '2018-02-27 09:02:00',
...                                           '2018-02-27 09:03:00',
...                                           '2018-02-27 09:04:00',
...                                           '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']),
...         subset=['a'])
                         a   b
2018-02-27 09:03:30   30.0 NaN
2018-02-27 09:04:30   40.0 NaN
method

at_time(time, asof=False, axis=None)

Select values at particular time of day (e.g., 9:30AM).

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

between_time : Select values between particular times of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_at_time : Get just the index locations for values at particular time of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='12H')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-09 12:00:00  2
2018-04-10 00:00:00  3
2018-04-10 12:00:00  4
>>> ts.at_time('12:00')
                     A
2018-04-09 12:00:00  2
2018-04-10 12:00:00  4
method

between_time(start_time, end_time, include_start=True, include_end=True, axis=None)

Select values between particular times of the day (e.g., 9:00-9:30 AM).

By setting start_time to be later than end_time, you can get the times that are not between the two times.

Parameters
  • start_time (datetime.time or str) Initial time as a time filter limit.
  • end_time (datetime.time or str) End time as a time filter limit.
  • include_start (bool, default True) Whether the start time needs to be included in the result.
  • include_end (bool, default True) Whether the end time needs to be included in the result.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Determine range time on index or columns value.
Returns (Series or DataFrame)

Data from the original object filtered to the specified dates range.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

at_time : Select values at a particular time of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4
>>> ts.between_time('0:15', '0:45')
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are not between two times by setting start_time later than end_time:

>>> ts.between_time('0:45', '0:15')
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4
method

first(offset)

Select initial periods of time series data based on a date offset.

When having a DataFrame with dates as index, this function can select the first few rows based on a date offset.

Parameters
  • offset (str, DateOffset or dateutil.relativedelta) The offset length of the data that will be selected. For instance,'1M' will display all the rows having their index within the first month.
Returns (Series or DataFrame)

A subset of the caller.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

last : Select final periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the first 3 days:

>>> ts.first('3D')
            A
2018-04-09  1
2018-04-11  2

Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.

method

last(offset)

Select final periods of time series data based on a date offset.

For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.

Parameters
  • offset (str, DateOffset, dateutil.relativedelta) The offset length of the data that will be selected. For instance,'3D' will display all the rows having their index within the last 3 days.
Returns (Series or DataFrame)

A subset of the caller.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

first : Select initial periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the last 3 days:

>>> ts.last('3D')
            A
2018-04-13  3
2018-04-15  4

Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.

method

rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Index to direct ranking.
  • method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') How to rank the group of records that have the same value (i.e. ties):
    • average: average rank of the group
    • min: lowest rank in the group
    • max: highest rank in the group
    • first: ranks assigned in order they appear in the array
    • dense: like 'min', but rank always increases by 1 between groups.
  • numeric_only (bool, optional) For DataFrame objects, rank only numeric columns if set to True.
  • na_option ({'keep', 'top', 'bottom'}, default 'keep') How to rank NaN values:
    • keep: assign NaN rank to NaN values
    • top: assign lowest rank to NaN values
    • bottom: assign highest rank to NaN values
  • ascending (bool, default True) Whether or not the elements should be ranked in ascending order.
  • pct (bool, default False) Whether or not to display the returned rankings in percentileform.
Returns (same type as caller)

Return a Series or DataFrame with data ranks as values.

See Also

core.groupby.GroupBy.rank : Rank of values within each group.

Examples
>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

The following example shows how the method behaves with the above parameters:

  • default_rank: this is the default behaviour obtained without using any parameter.
  • max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since 'cat' and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
  • NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.
  • pct_rank: when setting pct = True, the ranking is expressed as percentile rank.
>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN
method

slice_shift(periods=1, axis=0)

Equivalent to shift without copying data.The shifted data will not include the dropped periods and the shifted axis will be smaller than the original.

.. deprecated:: 1.2.0 slice_shift is deprecated, use DataFrame/Series.shift instead.

Parameters
  • periods (int) Number of periods to move, can be positive or negative.

Notes

While the slice_shift is faster than shift, you may pay for it later during alignment.

method

tshift(periods=1, freq=None, axis=0)

Shift the time index, using the index's frequency if available.

.. deprecated:: 1.1.0 Use shift instead.

Parameters
  • periods (int) Number of periods to move, can be positive or negative.
  • freq (DateOffset, timedelta, or str, default None) Increment to use from the tseries moduleor time rule expressed as a string (e.g. 'EOM').
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default 0) Corresponds to the axis that contains the Index.

Notes

If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown

method

truncate(before=None, after=None, axis=None, copy=True)

Truncate a Series or DataFrame before and after some index value.

This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.

Parameters
  • before (date, str, int) Truncate all rows before this index value.
  • after (date, str, int) Truncate all rows after this index value.
  • axis ({0 or 'index', 1 or 'columns'}, optional) Axis to truncate. Truncates the index (rows) by default.
  • copy (bool, default is True,) Return a copy of the truncated section.
Returns (type of caller)

The truncated Series or DataFrame.

See Also

DataFrame.loc : Select a subset of a DataFrame by label.DataFrame.iloc : Select a subset of a DataFrame by position.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],...                    'B': ['f', 'g', 'h', 'i', 'j'],
...                    'C': ['k', 'l', 'm', 'n', 'o']},
...                   index=[1, 2, 3, 4, 5])
>>> df
   A  B  C
1  a  f  k
2  b  g  l
3  c  h  m
4  d  i  n
5  e  j  o
>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n

The columns of a DataFrame can be truncated.

>>> df.truncate(before="A", after="B", axis="columns")
   A  B
1  a  f
2  b  g
3  c  h
4  d  i
5  e  j

For Series, only rows can be truncated.

>>> df['A'].truncate(before=2, after=4)
2    b
3    c
4    d
Name: A, dtype: object

The index values in truncate can be datetimes or string dates.

>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
                     A
2016-01-31 23:59:56  1
2016-01-31 23:59:57  1
2016-01-31 23:59:58  1
2016-01-31 23:59:59  1
2016-02-01 00:00:00  1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
...             after=pd.Timestamp('2016-01-10')).tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

>>> df.truncate('2016-01-05', '2016-01-10').tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Note that truncate assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

>>> df.loc['2016-01-05':'2016-01-10', :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1
method

tz_convert(tz, axis=0, level=None, copy=True)

Convert tz-aware axis to target time zone.

Parameters
  • level (int, str, default None) If axis is a MultiIndex, convert a specific level. Otherwisemust be None.
  • copy (bool, default True) Also make a copy of the underlying data.
Returns ({klass})

Object with time zone converted axis.

Raises
  • TypeError If the axis is tz-naive.
method

tz_localize(tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise')

Localize tz-naive index of a Series or DataFrame to target time zone.

This operation localizes the Index. To localize the values in a timezone-naive Series, use :meth:Series.dt.tz_localize.

Parameters
  • level (int, str, default None) If axis ia a MultiIndex, localize a specific level. Otherwisemust be None.
  • copy (bool, default True) Also make a copy of the underlying data.
  • ambiguous ('infer', bool-ndarray, 'NaT', default 'raise') When clocks moved backward due to DST, ambiguous times may arise.For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.
    • 'infer' will attempt to infer fall dst-transition hours based on order
    • bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
    • 'NaT' will return NaT where there are ambiguous times
    • 'raise' will raise an AmbiguousTimeError if there are ambiguous times.
  • nonexistent (str, default 'raise') A nonexistent time does not exist in a particular timezonewhere clocks moved forward due to DST. Valid values are:
    • 'shift_forward' will shift the nonexistent time forward to the closest existing time
    • 'shift_backward' will shift the nonexistent time backward to the closest existing time
    • 'NaT' will return NaT where there are nonexistent times
    • timedelta objects will shift nonexistent times by the timedelta
    • 'raise' will raise an NonExistentTimeError if there are nonexistent times.
Returns (Series or DataFrame)

Same type as the input.

Raises
  • TypeError If the TimeSeries is tz-aware and tz is not None.
Examples

Localize local times:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00']))
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00    1
dtype: int64

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.Series(range(7),
...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 03:00:00',
...                                       '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00    0
2018-10-28 02:00:00+02:00    1
2018-10-28 02:30:00+02:00    2
2018-10-28 02:00:00+01:00    3
2018-10-28 02:30:00+01:00    4
2018-10-28 03:00:00+01:00    5
2018-10-28 03:30:00+01:00    6
dtype: int64

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.Series(range(3),
...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
...                                       '2018-10-28 02:36:00',
...                                       '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00    0
2018-10-28 02:36:00+02:00    1
2018-10-28 03:46:00+01:00    2
dtype: int64

If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or 'shift_forward' or 'shift_backward'.

>>> s = pd.Series(range(2),
...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
...                                       '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00    0
2015-03-29 03:30:00+02:00              1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))
2015-03-29 03:30:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
method

abs()

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns (abs)

Series/DataFrame containing the absolute value of each element.

See Also

numpy.absolute : Calculate the absolute value element-wise.

Notes

For complex inputs, 1.2 + 1j, the absolute value is :math:\sqrt{ a^2 + b^2 }.

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0   1 days
dtype: timedelta64[ns]

Select rows with data closest to certain value using argsort (from StackOverflow <https://stackoverflow.com/a/17758115>__).

>>> df = pd.DataFrame({
...     'a': [4, 5, 6, 7],
...     'b': [10, 20, 30, 40],
...     'c': [100, 50, -30, -50]
... })
>>> df
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50
method

describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters
  • percentiles (list-like of numbers, optional) The percentiles to include in the output. All shouldfall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
  • include ('all', list-like of dtypes or None (default), optional) A white list of data types to include in the result. Ignoredfor Series. Here are the options:
    • 'all' : All columns of the input will be included in the output.
    • A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
    • None (default) : The result will include all numeric columns.
  • exclude (list-like of dtypes or None (default), optional,) A black list of data types to omit from the result. Ignoredfor Series. Here are the options:
    • A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To exclude pandas categorical columns, use 'category'
    • None (default) : The result will exclude nothing.
  • datetime_is_numeric (bool, default False) Whether to treat datetime dtypes as numeric. This affects statisticscalculated for the column. For DataFrame input, this also controls whether datetime columns are included by default.
    .. versionadded:: 1.1.0
Returns (Series or DataFrame)

Summary statistics of the Series or Dataframe provided.

See Also

DataFrame.count: Count number of non-NA/null observations.DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype.

Notes

For numeric data, the result's index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result's index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value's frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01")
... ])
>>> s.describe(datetime_is_numeric=True)
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                   })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')  # doctest: +SKIP
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])  # doctest: +SKIP
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])
       categorical
count            3
unique           3
top              d
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])  # doctest: +SKIP
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])  # doctest: +SKIP
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0
method

pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)

Percentage change between the current and a prior element.

Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements.

Parameters
  • periods (int, default 1) Periods to shift for forming percent change.
  • fill_method (str, default 'pad') How to handle NAs before computing percent changes.
  • limit (int, default None) The number of consecutive NAs to fill before stopping.
  • freq (DateOffset, timedelta, or str, optional) Increment to use from time series API (e.g. 'M' or BDay()).
  • **kwargs Additional keyword arguments are passed intoDataFrame.shift or Series.shift.
Returns (chg : Series or DataFrame)

The same type as the calling object.

See Also

Series.diff : Compute the difference of two elements in a Series.DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64
>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13
>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351
>>> df.pct_change(axis='columns', periods=-1)
          2016      2015  2014
GOOG  0.179241  0.094112   NaN
APPL -0.252395 -0.011860   NaN
method

any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) Indicate which axis or axes should be reduced.
    • 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
    • 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
    • None : reduce all axes, return a scalar.
  • bool_only (bool, default None) Include only boolean columns. If None, will attempt to use everything,then use only boolean data. Not implemented for Series.
  • skipna (bool, default True) Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • **kwargs (any, default None) Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.

See Also

numpy.any : Numpy version of this method.Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
   A  B  C
0  1  0  0
1  2  2  0
>>> df.any()
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2
>>> df.any(axis='columns')
0    True
1    True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0
>>> df.any(axis='columns')
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)
method

all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) Indicate which axis or axes should be reduced.
    • 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
    • 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
    • None : reduce all axes, return a scalar.
  • bool_only (bool, default None) Include only boolean columns. If None, will attempt to use everything,then use only boolean data. Not implemented for Series.
  • skipna (bool, default True) Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • **kwargs (any, default None) Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.

See Also

Series.all : Return True if all elements are True.DataFrame.any : Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if column-wise values all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if row-wise values all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False
method

mad(axis=None, skipna=None, level=None)

Return the mean absolute deviation of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default None) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
method

sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

cummin(axis=None, skipna=True, *args, **kwargs)

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative minimum of Series or DataFrame.

See Also

core.window.Expanding.min : Similar functionality but ignores NaN values. DataFrame.min : Return the minimum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
0    2.0
1    NaN
2    2.0
3   -1.0
4   -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0
method

cummax(axis=None, skipna=True, *args, **kwargs)

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative maximum of Series or DataFrame.

See Also

core.window.Expanding.max : Similar functionality but ignores NaN values. DataFrame.max : Return the maximum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
0    2.0
1    NaN
2    5.0
3    5.0
4    5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0
method

cumsum(axis=None, skipna=True, *args, **kwargs)

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative sum of Series or DataFrame.

See Also

core.window.Expanding.sum : Similar functionality but ignores NaN values. DataFrame.sum : Return the sum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
0    2.0
1    NaN
2    7.0
3    6.0
4    6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0
method

cumprod(axis=None, skipna=True, *args, **kwargs)

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative product of Series or DataFrame.

See Also

core.window.Expanding.prod : Similar functionality but ignores NaN values. DataFrame.prod : Return the product over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
0     2.0
1     NaN
2    10.0
3   -10.0
4    -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0
method

sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • min_count (int, default 0) The required number of valid values to perform the operation. If fewer thanmin_count non-NA values are present the result will be NA.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.sum()
14

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
0.0

This can be controlled with the min_count parameter. For example, if you'd like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([], dtype="float64").sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nan
method

prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the product of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • min_count (int, default 0) The required number of valid values to perform the operation. If fewer thanmin_count non-NA values are present the result will be NA.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan
method

mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the mean of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the median of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.max()
8
method

min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.min()
0
method

rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, method='single')

Provide rolling window calculations.

Parameters
  • window (int, offset, or BaseIndexer subclass) Size of the moving window. This is the number of observations used forcalculating the statistic. Each window will be a fixed size.
    If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes.
    If a BaseIndexer subclass is passed, calculates the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, and closed will be passed to get_window_bounds.
  • min_periods (int, default None) Minimum number of observations in window required to have a value(otherwise result is NA). For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window.
  • center (bool, default False) Set the labels at the center of the window.
  • win_type (str, default None) Provide a window type. If None, all points are evenly weighted.See the notes below for further information.
  • on (str, optional) For a DataFrame, a datetime-like column or Index level on whichto calculate the rolling window, rather than the DataFrame's index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.
  • closed (str, default None) Make the interval closed on the 'right', 'left', 'both' or'neither' endpoints. Defaults to 'right'.
    .. versionchanged:: 1.2.0
    The closed parameter with fixed windows is now supported.
    
  • method (str {'single', 'table'}, default 'single') Execute the rolling operation per single column or row ('single')or over the entire object ('table').
    This argument is only implemented when specifying engine='numba' in the method call.
    .. versionadded:: 1.3.0
See Also

expanding : Provides expanding transformations.ewm : Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

To learn more about the offsets & frequency strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.

If win_type=None, all points are evenly weighted; otherwise, win_type can accept a string of any scipy.signal window function <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>__.

Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature. Please see the third example below on how to add the additional parameters.

Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

Rolling sum with a window length of 2, using the 'triang' window type.

>>> df.rolling(2, win_type='triang').sum()
     B
0  NaN
1  0.5
2  1.5
3  NaN
4  NaN

Rolling sum with a window length of 2, using the 'gaussian' window type (note how we need to specify std).

>>> df.rolling(2, win_type='gaussian').sum(std=3)
          B
0       NaN
1  0.986207
2  2.958621
3       NaN
4       NaN

Rolling sum with a window length of 2, min_periods defaults to the window length.

>>> df.rolling(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  NaN
4  NaN

Same as above, but explicitly set the min_periods

>>> df.rolling(2, min_periods=1).sum()
     B
0  0.0
1  1.0
2  3.0
3  2.0
4  4.0

Same as above, but with forward-looking windows

>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

A ragged (meaning not-a-regular frequency), time-indexed DataFrame

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
...                   index = [pd.Timestamp('20130101 09:00:00'),
...                            pd.Timestamp('20130101 09:00:02'),
...                            pd.Timestamp('20130101 09:00:03'),
...                            pd.Timestamp('20130101 09:00:05'),
...                            pd.Timestamp('20130101 09:00:06')])
>>> df
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. The default for min_periods is 1.

>>> df.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0
method

expanding(min_periods=1, center=None, axis=0, method='single')

Provide expanding transformations.

Parameters
  • min_periods (int, default 1) Minimum number of observations in window required to have a value(otherwise result is NA).
  • center (bool, default False) Set the labels at the center of the window.
  • method (str {'single', 'table'}, default 'single') Execute the rolling operation per single column or row ('single')or over the entire object ('table').
    This argument is only implemented when specifying engine='numba' in the method call.
    .. versionadded:: 1.3.0
See Also

rolling : Provides rolling window calculations.ewm : Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

Examples
>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.expanding(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  3.0
4  7.0
method

ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, times=None)

Provide exponential weighted (EW) functions.

Available EW functions: mean(), var(), std(), corr(), cov().

Exactly one parameter: com, span, halflife, or alpha must be provided.

Parameters
  • com (float, optional) Specify decay in terms of center of mass,:math:\alpha = 1 / (1 + com), for :math:com \geq 0.
  • span (float, optional) Specify decay in terms of span,:math:\alpha = 2 / (span + 1), for :math:span \geq 1.
  • halflife (float, str, timedelta, optional) Specify decay in terms of half-life,:math:\alpha = 1 - \exp\left(-\ln(2) / halflife\right), for :math:halflife > 0.
    If times is specified, the time unit (str or timedelta) over which an observation decays to half its value. Only applicable to mean() and halflife value will not apply to the other functions.
    .. versionadded:: 1.1.0
  • alpha (float, optional) Specify smoothing factor :math:\alpha directly,:math:0 < \alpha \leq 1.
  • min_periods (int, default 0) Minimum number of observations in window required to have a value(otherwise result is NA).
  • adjust (bool, default True) Divide by decaying adjustment factor in beginning periods to accountfor imbalance in relative weightings (viewing EWMA as a moving average).
    • When adjust=True (default), the EW function is calculated using weights :math:w_i = (1 - \alpha)^i. For example, the EW moving average of the series [:math:x_0, x_1, ..., x_t] would be:
    .. math:: y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
    • When adjust=False, the exponentially weighted function is calculated recursively:
    .. math:: \begin{split} y_0 &= x_0\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}
  • ignore_na (bool, default False) Ignore missing values when calculating weights; specify True to reproducepre-0.15.0 behavior.
    • When ignore_na=False (default), weights are based on absolute positions. For example, the weights of :math:x_0 and :math:x_2 used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:(1-\alpha)^2 and :math:1 if adjust=True, and :math:(1-\alpha)^2 and :math:\alpha if adjust=False.
    • When ignore_na=True (reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of :math:x_0 and :math:x_2 used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:1-\alpha and :math:1 if adjust=True, and :math:1-\alpha and :math:\alpha if adjust=False.
  • axis ({0, 1}, default 0) The axis to use. The value 0 identifies the rows, and 1identifies the columns.
  • times (str, np.ndarray, Series, default None) 0
    d .
    .
    .
    .
Returns (DataFrame)

A Window sub-classed for the particular operation.

See Also

rolling : Provides rolling window calculations.expanding : Provides expanding transformations.

Notes

: .

Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.ewm(com=0.5).mean()
          B
0  0.000000
1  0.750000
2  1.615385
3  1.615385
4  3.670213

Specifying times with a timedelta halflife when computing mean.

>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
          B
0  0.000000
1  0.585786
2  1.523889
3  1.523889
4  3.233686
method

first_valid_index()

Return index for first non-NA value or None, if no NA value is found.

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

method

last_valid_index()

Return index for last non-NA value or None, if no NA value is found.

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

method

__repr__() → str

Return a string representation for a particular DataFrame.

method

to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, min_rows=None, max_cols=None, show_dimensions=False, decimal='.', line_width=None, max_colwidth=None, encoding=None)

Render a DataFrame to a console-friendly tabular output.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (sequence, optional, default None) The subset of columns to write. Writes all columns by default.
  • col_space (int, list or dict of int, optional) The minimum width of each column.
  • header (bool or sequence, optional) Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
  • index (bool, optional, default True) Whether to print index (row) labels.
  • na_rep (str, optional, default 'NaN') String representation of NaN to use.
  • formatters (list, tuple or dict of one-param. functions, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
  • float_format (one-parameter function, optional, default None) Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
    .. versionchanged:: 1.2.0
  • sparsify (bool, optional, default True) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.
  • index_names (bool, optional, default True) Prints the names of the indexes.
  • justify (str, default None) How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
    • left
    • right
    • center
    • justify
    • justify-all
    • start
    • end
    • inherit
    • match-parent
    • initial
    • unset.
  • max_rows (int, optional) Maximum number of rows to display in the console.
  • min_rows (int, optional) The number of rows to display in the console in a truncated repr(when number of rows is above max_rows).
  • max_cols (int, optional) Maximum number of columns to display in the console.
  • show_dimensions (bool, default False) Display DataFrame dimensions (number of rows by number of columns).
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • line_width (int, optional) Width to wrap a line in characters.
  • max_colwidth (int, optional) Max width to truncate each column in characters. By default, no limit.
    .. versionadded:: 1.0.0
  • encoding (str, default "utf-8") Set character encoding.
    .. versionadded:: 1.0
Returns (str or None)

If buf is None, returns the result as a string. Otherwise returnsNone.

See Also

to_html : Convert DataFrame to HTML.

Examples
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}>>> df = pd.DataFrame(d)
>>> print(df.to_string())
   col1  col2
0     1     4
1     2     5
2     3     6
generator

items()

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields (label : object)

The column names for the DataFrame being iterated over.ent : Series The column entries belonging to each label, as a Series.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.

Examples
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
generator

iteritems()

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields (label : object)

The column names for the DataFrame being iterated over.ent : Series The column entries belonging to each label, as a Series.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.

Examples
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
generator

iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

Yields (index : label or tuple of label)

The index of the row. A tuple for a MultiIndex. : Series The data of the row as a Series.

See Also

DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.DataFrame.items : Iterate over (column name, Series) pairs.

Notes

  1. Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

To preserve dtypes while iterating over the rows, it is better to use :meth:itertuples which returns namedtuples of the values and which is generally faster than iterrows.

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

method

itertuples(index=True, name='Pandas')

Iterate over DataFrame rows as namedtuples.

Parameters
  • index (bool, default True) If True, return the index as the first element of the tuple.
  • name (str or None, default "Pandas") The name of the returned namedtuples or None to return regulartuples.
Returns (iterator)

An object to iterate over namedtuples for each row in theDataFrame with the first field possibly being the index and following fields being the column values.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.items : Iterate over (column name, Series) pairs.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. On python versions < 3.7 regular tuples are returned for DataFrames with a large number of columns (>254).

Examples
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},...                   index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):
...     print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name='Animal'):
...     print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
method

__len__() → int

Returns length of info axis, but here we use the index.

method

dot(other)

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using self @ other in Python >= 3.5.

Parameters
  • other (Series, DataFrame or array-like) The other object to compute the matrix product with.
Returns (Series or DataFrame)

If other is a Series, return the matrix product between self andother as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.

See Also

Series.dot: Similar method for Series.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0    -4
1     5
dtype: int64

Here we multiply a DataFrame with another DataFrame.

>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
    0   1
0   1   4
1   2   2

Note that the dot method give the same result as @

>>> df @ other
    0   1
0   1   4
1   2   2

The dot method works also if other is an np.array.

>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
    0   1
0   1   4
1   2   2

Note how shuffling of the objects does not change the result.

>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0    -4
1     5
dtype: int64
method

__matmul__(other) → DataFrame or Series

Matrix multiplication using binary @ operator in Python>=3.5.

method

__rmatmul__(other)

Matrix multiplication using binary @ operator in Python>=3.5.

classmethod

from_dict(data, orient='columns', dtype=None, columns=None)

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters
  • data (dict) Of the form {field : array-like} or {field : dict}.
  • orient ({'columns', 'index'}, default 'columns') The "orientation" of the data. If the keys of the passed dictshould be the columns of the resulting DataFrame, pass 'columns' (default). Otherwise if the keys should be rows, pass 'index'.
  • dtype (dtype, default None) Data type to force, otherwise infer.
  • columns (list, default None) Column labels to use when orient='index'. Raises a ValueErrorif used with orient='columns'.
See Also

DataFrame.from_records : DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame. DataFrame : DataFrame object creation using constructor.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
       0  1  2  3
row_1  3  2  1  0
row_2  a  b  c  d

When using the 'index' orientation, the column names can be specified manually:

>>> pd.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D'])
       A  B  C  D
row_1  3  2  1  0
row_2  a  b  c  d
method

to_numpy(dtype=None, copy=False, na_value=<no_default>)

Convert the DataFrame to a NumPy array.

By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are float16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.

Parameters
  • dtype (str or numpy.dtype, optional) The dtype to pass to :meth:numpy.asarray.
  • copy (bool, default False) Whether to ensure that the returned value is not a view onanother array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.
  • na_value (Any, optional) The value to use for missing values. The default value dependson dtype and the dtypes of the DataFrame columns.
    .. versionadded:: 1.1.0
See Also

Series.to_numpy : Similar method for Series.

Examples
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()array([[1, 3],
       [2, 4]])

With heterogeneous data, the lowest common type will have to be used.

>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
       [2. , 4.5]])

For a mix of numeric and non-numeric types, the output array will have object dtype.

>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
       [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
method

to_dict(orient='dict', into=<class 'dict'>)

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Parameters
  • orient (str {'dict', 'list', 'series', 'split', 'records', 'index'}) Determines the type of the values of the dictionary.
    • 'dict' (default) : dict like {column -> {index -> value}}
    • 'list' : dict like {column -> [values]}
    • 'series' : dict like {column -> Series(values)}
    • 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
    • 'records' : list like [{column -> value}, ... , {column -> value}]
    • 'index' : dict like {index -> {column -> value}}
    Abbreviations are allowed. s indicates series and sp indicates split.
  • into (class, default dict) The collections.abc.Mapping subclass used for all Mappingsin the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.
Returns (dict, list or collections.abc.Mapping)

Return a collections.abc.Mapping object representing the DataFrame.The resulting transformation depends on the orient parameter.

See Also

DataFrame.from_dict: Create a DataFrame from a dictionary.DataFrame.to_json: Convert a DataFrame to JSON format.

Examples
>>> df = pd.DataFrame({'col1': [1, 2],...                    'col2': [0.5, 0.75]},
...                   index=['row1', 'row2'])
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
method

to_gbq(destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=False, table_schema=None, location=None, progress_bar=True, credentials=None)

Write a DataFrame to a Google BigQuery table.

This function requires the pandas-gbq package <https://pandas-gbq.readthedocs.io>__.

See the How to authenticate with Google BigQuery <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>__ guide for authentication instructions.

Parameters
  • destination_table (str) Name of table to be written, in the form dataset.tablename.
  • project_id (str, optional) Google BigQuery Account project ID. Optional when available fromthe environment.
  • chunksize (int, optional) Number of rows to be inserted in each chunk from the dataframe.Set to None to load the whole dataframe at once.
  • reauth (bool, default False) Force Google BigQuery to re-authenticate the user. This is usefulif multiple accounts are used.
  • if_exists (str, default 'fail') Behavior when the destination table exists. Value can be one of:
    'fail' If table exists raise pandasgbq.gbq.TableCreationError. 'replace' If table exists, drop it, recreate it, and insert data. 'append' If table exists, insert data. Create if does not exist.
  • auth_local_webserver (bool, default False) Use the local webserver flow instead of the console flowwhen getting user credentials.
    .. _local webserver flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server .. _console flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
    New in version 0.2.0 of pandas-gbq.
  • table_schema (list of dicts, optional) List of BigQuery table fields to which according DataFramecolumns conform to, e.g. [{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field.
    New in version 0.3.1 of pandas-gbq.
  • location (str, optional) Location where the load job should run. See the BigQuery locationsdocumentation <https://cloud.google.com/bigquery/docs/dataset-locations>__ for a list of available locations. The location must match that of the target dataset.
    New in version 0.5.0 of pandas-gbq.
  • progress_bar (bool, default True) Use the library tqdm to show the progress bar for the upload,chunk by chunk.
    New in version 0.5.0 of pandas-gbq.
  • credentials (google.auth.credentials.Credentials, optional) Credentials for accessing Google APIs. Use this parameter tooverride default credentials, such as to use Compute Engine :class:google.auth.compute_engine.Credentials or Service Account :class:google.oauth2.service_account.Credentials directly.
    New in version 0.8.0 of pandas-gbq.
See Also

pandas_gbq.to_gbq : This function in the pandas-gbq library.read_gbq : Read a DataFrame from Google BigQuery.

classmethod

from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)

Convert structured or record ndarray to DataFrame.

Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame.

Parameters
  • data (structured ndarray, sequence of tuples or dicts, or DataFrame) Structured input data.
  • index (str, list of fields, array-like) Field of array to use as the index, alternately a specific set ofinput labels to use.
  • exclude (sequence, default None) Columns or fields to exclude.
  • columns (sequence, default None) Column names to use. If the passed data do not have namesassociated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).
  • coerce_float (bool, default False) Attempt to convert values of non-string, non-numeric objects (likedecimal.Decimal) to floating point, useful for SQL result sets.
  • nrows (int, default None) Number of rows to read if data is an iterator.
See Also

DataFrame.from_dict : DataFrame from dict of array-like or dicts.DataFrame : DataFrame object creation using constructor.

Examples

Data can be provided as a structured ndarray:

>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of dicts:

>>> data = [{'col_1': 3, 'col_2': 'a'},
...         {'col_1': 2, 'col_2': 'b'},
...         {'col_1': 1, 'col_2': 'c'},
...         {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of tuples with corresponding columns:

>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d
method

to_records(index=True, column_dtypes=None, index_dtypes=None)

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters
  • index (bool, default True) Include index in resulting record array, stored in 'index'field or using the index label, if set.
  • column_dtypes (str, type, dict, default None) If a string or type, the data type to store all columns. Ifa dictionary, a mapping of column names and indices (zero-indexed) to specific data types.
  • index_dtypes (str, type, dict, default None) If a string or type, the data type to store all index levels. Ifa dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.
    This mapping is applied only if index=True.
Returns (numpy.recarray)

NumPy ndarray with the DataFrame labels as fields and each rowof the DataFrame as entries.

See Also

DataFrame.from_records: Convert structured or record ndarray to DataFrame. numpy.recarray: An ndarray that allows field access using attributes, analogous to typed columns in a spreadsheet.

Examples
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},...                   index=['a', 'b'])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to 'index'. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
method

to_stata(path, convert_dates=None, write_index=True, byteorder=None, time_stamp=None, data_label=None, variable_labels=None, version=114, convert_strl=None, compression='infer', storage_options=None)

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. "dta" files contain a Stata dataset.

Parameters
  • path (str, buffer or path object) String, path object (pathlib.Path or py._path.local.LocalPath) orobject implementing a binary write() function. If using a buffer then the buffer will not be automatically closed after the file data has been written.
    .. versionchanged:: 1.0.0
    Previously this was "fname"
  • convert_dates (dict) Dictionary mapping columns containing datetime types to statainternal format to use when writing the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to 'tc'. Raises NotImplementedError if a datetime column has timezone information.
  • write_index (bool) Write the index to Stata dataset.
  • byteorder (str) Can be ">", "<", "little", or "big". default is sys.byteorder.
  • time_stamp (datetime) A datetime to use as file creation date. Default is the currenttime.
  • data_label (str, optional) A label for the data set. Must be 80 characters or smaller.
  • variable_labels (dict) Dictionary containing columns as keys and variable labels asvalues. Each label must be 80 characters or smaller.
  • version ({{114, 117, 118, 119, None}}, default 114) Version to use in the output dta file. Set to None to let pandasdecide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.
    Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.
    .. versionchanged:: 1.0.0
    Added support for formats 118 and 119.
    
  • convert_strl (list, optional) List of column names to convert to string columns to Stata StrLformat. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.
  • compression (str or dict, default 'infer') For on-the-fly compression of the output dta. If string, specifiescompression mode. If dict, value at key 'method' specifies compression mode. Compression mode must be one of {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}. If compression mode is 'infer' and fname is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no compression). If dict and compression mode is one of {{'zip', 'gzip', 'bz2'}}, or inferred as one of the above, other entries passed as additional compression options.
    .. versionadded:: 1.1.0
Raises
  • NotImplementedError
    • If datetimes contain timezone information
    • Column dtype is not representable in Stata
  • ValueError
    • Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime
    • Column listed in convert_dates is not in DataFrame
    • Categorical label contains more than 32,000 characters
See Also

read_stata : Import Stata data files.io.stata.StataWriter : Low-level writer for Stata data files. io.stata.StataWriter117 : Low-level writer for version 117 files.

Examples
>>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',...                               'parrot'],
...                    'speed': [350, 18, 361, 15]}})
>>> df.to_stata('animals.dta')  # doctest: +SKIP
method

to_feather(path, **kwargs)

Write a DataFrame to the binary Feather format.

Parameters
  • path (str or file-like object) If a string, it will be used as Root Directory path.
  • **kwargs Additional keywords passed to :func:pyarrow.feather.write_feather.Starting with pyarrow 0.17, this includes the compression, compression_level, chunksize and version keywords.
    .. versionadded:: 1.1.0
method

to_markdown(buf=None, mode='wt', index=True, storage_options=None, **kwargs)

Print DataFrame in Markdown-friendly format.

.. versionadded:: 1.0.0

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • mode (str, optional) Mode in which file is opened, "wt" by default.
  • index (bool, optional, default True) Add index (row) labels.
    .. versionadded:: 1.1.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
  • **kwargs These parameters will be passed to tabulate <https://pypi.org/project/tabulate>_.
Returns (str)

DataFrame in Markdown-friendly format.

Notes

Requires the tabulate <https://pypi.org/project/tabulate>_ package.

Examples
>>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")>>> print(s.to_markdown())
|    | animal   |
|---:|:---------|
|  0 | elk      |
|  1 | pig      |
|  2 | dog      |
|  3 | quetzal  |

Output markdown with a tabulate option.

>>> print(s.to_markdown(tablefmt="grid"))
+----+----------+
|    | animal   |
+====+==========+
|  0 | elk      |
+----+----------+
|  1 | pig      |
+----+----------+
|  2 | dog      |
+----+----------+
|  3 | quetzal  |
+----+----------+
method

to_parquet(path=None, engine='auto', compression='snappy', index=None, partition_cols=None, storage_options=None, **kwargs)

Write a DataFrame to the binary parquet format.

This function writes the dataframe as a parquet file <https://parquet.apache.org/>_. You can choose different parquet backends, and have the option of compression. See :ref:the user guide <io.parquet> for more details.

Parameters
  • path (str or file-like object, default None) If a string, it will be used as Root Directory pathwhen writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function) or io.BytesIO. The engine fastparquet does not accept file-like objects. If path is None, a bytes object is returned.
    .. versionchanged:: 1.2.0
    Previously this was "fname"
  • engine ({{'auto', 'pyarrow', 'fastparquet'}}, default 'auto') Parquet library to use. If 'auto', then the optionio.parquet.engine is used. The default io.parquet.engine behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable.
  • compression ({{'snappy', 'gzip', 'brotli', None}}, default 'snappy') Name of the compression to use. Use None for no compression.
  • index (bool, default None) If True, include the dataframe's index(es) in the file output.If False, they will not be written to the file. If None, similar to True the dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output.
  • partition_cols (list, optional, default None) Column names by which to partition the dataset.Columns are partitioned in the order they are given. Must be None if path is not a string.
  • **kwargs Additional arguments passed to the parquet library. See:ref:pandas io <io.parquet> for more details.
See Also

read_parquet : Read a parquet file.DataFrame.to_csv : Write a csv file. DataFrame.to_sql : Write to a sql table. DataFrame.to_hdf : Write to hdf.

Notes

This function requires either the fastparquet <https://pypi.org/project/fastparquet> or pyarrow <https://arrow.apache.org/docs/python/> library.

Examples
>>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})>>> df.to_parquet('df.parquet.gzip',
...               compression='gzip')  # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip')  # doctest: +SKIP
   col1  col2
0     1     3
1     2     4

If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don't use partition_cols, which creates multiple files.

>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()
method

to_html(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', bold_rows=True, classes=None, escape=True, notebook=False, border=None, table_id=None, render_links=False, encoding=None)

Render a DataFrame as an HTML table.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (sequence, optional, default None) The subset of columns to write. Writes all columns by default.
  • col_space (str or int, list or dict of int or str, optional) The minimum width of each column in CSS length units. An int is assumed to be px units.
    .. versionadded:: 0.25.0 Ability to use str.
  • header (bool, optional) Whether to print column labels, default True.
  • index (bool, optional, default True) Whether to print index (row) labels.
  • na_rep (str, optional, default 'NaN') String representation of NaN to use.
  • formatters (list, tuple or dict of one-param. functions, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
  • float_format (one-parameter function, optional, default None) Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
    .. versionchanged:: 1.2.0
  • sparsify (bool, optional, default True) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.
  • index_names (bool, optional, default True) Prints the names of the indexes.
  • justify (str, default None) How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
    • left
    • right
    • center
    • justify
    • justify-all
    • start
    • end
    • inherit
    • match-parent
    • initial
    • unset.
  • max_rows (int, optional) Maximum number of rows to display in the console.
  • max_cols (int, optional) Maximum number of columns to display in the console.
  • show_dimensions (bool, default False) Display DataFrame dimensions (number of rows by number of columns).
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • bold_rows (bool, default True) Make the row labels bold in the output.
  • classes (str or list or tuple, default None) CSS class(es) to apply to the resulting html table.
  • escape (bool, default True) Convert the characters <, >, and & to HTML-safe sequences.
  • notebook ({True, False}, default False) Whether the generated HTML is for IPython Notebook.
  • border (int) A border=border attribute is included in the opening<table> tag. Default pd.options.display.html.border.
  • table_id (str, optional) A css id is included in the opening <table> tag if specified.
  • render_links (bool, default False) Convert URLs to HTML links.
  • encoding (str, default "utf-8") Set character encoding.
    .. versionadded:: 1.0
  • min_rows (int, optional) The number of rows to display in the console in a truncated repr(when number of rows is above max_rows).
Returns (str or None)

If buf is None, returns the result as a string. Otherwise returnsNone.

See Also

to_string : Convert DataFrame to a string.

method

to_xml(path_or_buffer=None, index=True, root_name='data', row_name='row', na_rep=None, attr_cols=None, elem_cols=None, namespaces=None, prefix=None, encoding='utf-8', xml_declaration=True, pretty_print=True, parser='lxml', stylesheet=None, compression='infer', storage_options=None)

Render a DataFrame to an XML document.

.. versionadded:: 1.3.0

Parameters
  • path_or_buffer (str, path object or file-like object, optional) File to write output to. If None, the output is returned as astring.
  • index (bool, default True) Whether to include index in XML document.
  • root_name (str, default 'data') The name of root element in XML document.
  • row_name (str, default 'row') The name of row element in XML document.
  • na_rep (str, optional) Missing data representation.
  • attr_cols (list-like, optional) List of columns to write as attributes in row element.Hierarchical columns will be flattened with underscore delimiting the different levels.
  • elem_cols (list-like, optional) List of columns to write as children in row element. By default,all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels.
  • namespaces (dict, optional) All namespaces to be defined in root element. Keys of dictshould be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example, ::
    namespaces = {"": "https://example.com"}
    
  • prefix (str, optional) Namespace prefix to be used for every element and/or attributein document. This should be one of the keys in namespaces dict.
  • encoding (str, default 'utf-8') Encoding of the resulting document.
  • xml_declaration (bool, default True) Whether to include the XML declaration at start of document.
  • pretty_print (bool, default True) Whether output should be pretty printed with indentation andline breaks.
  • parser ({'lxml','etree'}, default 'lxml') Parser module to use for building of tree. Only 'lxml' and'etree' are supported. With 'lxml', the ability to use XSLT stylesheet is supported.
  • stylesheet (str, path object or file-like object, optional) A URL, file-like object, or a raw string containing an XSLTscript used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requires lxml to be installed. Only XSLT 1.0 scripts and not later versions is currently supported.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') For on-the-fly decompression of on-disk data. If 'infer', then usegzip, bz2, zip or xz if path_or_buffer is a string ending in '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
Returns (None or str)

If io is None, returns the resulting XML format as astring. Otherwise returns None.

See Also

to_json : Convert the pandas object to a JSON string.to_html : Convert DataFrame to a html.

Examples
>>> df = pd.DataFrame({'shape': ['square', 'circle', 'triangle'],...                    'degrees': [360, 360, 180],
...                    'sides': [4, np.nan, 3]})
>>> df.to_xml()  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row>
    <index>0</index>
    <shape>square</shape>
    <degrees>360</degrees>
    <sides>4.0</sides>
  </row>
  <row>
    <index>1</index>
    <shape>circle</shape>
    <degrees>360</degrees>
    <sides/>
  </row>
  <row>
    <index>2</index>
    <shape>triangle</shape>
    <degrees>180</degrees>
    <sides>3.0</sides>
  </row>
</data>
>>> df.to_xml(attr_cols=[
...           'index', 'shape', 'degrees', 'sides'
...           ])  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row index="0" shape="square" degrees="360" sides="4.0"/>
  <row index="1" shape="circle" degrees="360"/>
  <row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>
>>> df.to_xml(namespaces={"doc": "https://example.com"},
...           prefix="doc")  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
  <doc:row>
    <doc:index>0</doc:index>
    <doc:shape>square</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides>4.0</doc:sides>
  </doc:row>
  <doc:row>
    <doc:index>1</doc:index>
    <doc:shape>circle</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides/>
  </doc:row>
  <doc:row>
    <doc:index>2</doc:index>
    <doc:shape>triangle</doc:shape>
    <doc:degrees>180</doc:degrees>
    <doc:sides>3.0</doc:sides>
  </doc:row>
</doc:data>
method

info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None, null_counts=None)

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.

Parameters
  • verbose (bool, optional) Whether to print the full summary. By default, the setting inpandas.options.display.max_info_columns is followed.
  • buf (writable buffer, defaults to sys.stdout) Where to send the output. By default, the output is printed tosys.stdout. Pass a writable buffer if you need to further process the output.
  • max_cols (int, optional) When to switch from the verbose to the truncated output. If theDataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.
  • memory_usage (bool, str, optional) Specifies whether total memory usage of the DataFrameelements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting.
    True always show memory usage. False never shows memory usage. A value of 'deep' is equivalent to "True with deep introspection". Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources.
  • show_counts (bool, optional) Whether to show the non-null counts. By default, this is shownonly if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.
  • null_counts (bool, optional) .. deprecated:: 1.2.0 Use show_counts instead.
  • data (DataFrame) DataFrame to print information about.
Returns (None)

This method prints a summary of a DataFrame and returns None.

See Also

DataFrame.describe: Generate descriptive statistics of DataFrame columns. DataFrame.memory_usage: Memory usage of DataFrame columns.

Examples
>>> int_values = [1, 2, 3, 4, 5]>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
...                   "float_col": float_values})
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  # doctest: +SKIP
...     f.write(s)
260

The memory_usage parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 165.9 MB
method

memory_usage(index=True, deep=False)

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters
  • index (bool, default True) Specifies whether to include the memory usage of the DataFrame'sindex in returned Series. If index=True, the memory usage of the index is the first item in the output.
  • deep (bool, default False) If True, introspect the data deeply by interrogatingobject dtypes for system-level memory consumption, and include it in the returned values.
Returns (Series)

A Series whose index is the original column names and whose valuesis the memory usage of each column in bytes.

See Also

numpy.ndarray.nbytes : Total bytes consumed by the elements of an ndarray. Series.memory_usage : Bytes consumed by a Series. Categorical : Memory-efficient array for string values with many repeated values. DataFrame.info : Concise summary of a DataFrame.

Examples
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
...              for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
   int64  float64            complex128  object  bool
0      1      1.0              1.0+0.0j       1  True
1      1      1.0              1.0+0.0j       1  True
2      1      1.0              1.0+0.0j       1  True
3      1      1.0              1.0+0.0j       1  True
4      1      1.0              1.0+0.0j       1  True
>>> df.memory_usage()
Index           128
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64
>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)
Index            128
int64          40000
float64        40000
complex128     80000
object        180000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['object'].astype('category').memory_usage(deep=True)
5244
method

transpose(*args, copy=False)

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property :attr:.T is an accessor to the method :meth:transpose.

Parameters
  • *args (tuple, optional) Accepted for compatibility with NumPy.
  • copy (bool, default False) Whether to copy the data after transposing, even for DataFrameswith a single dtype.
    Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.
Returns (DataFrame)

The transposed DataFrame.

See Also

numpy.transpose : Permute the dimensions of a given array.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    int64
col2    int64
dtype: object
>>> df1_transposed.dtypes
0    int64
1    int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {'name': ['Alice', 'Bob'],
...       'score': [9.5, 8],
...       'employed': [False, True],
...       'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
    name  score  employed  kids
0  Alice    9.5     False     0
1    Bob    8.0      True     0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
              0     1
name      Alice   Bob
score       9.5   8.0
employed  False  True
kids          0     0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name         object
score       float64
employed       bool
kids          int64
dtype: object
>>> df2_transposed.dtypes
0    object
1    object
dtype: object
method

query(expr, inplace=False, **kwargs)

Query the columns of a DataFrame with a boolean expression.

Parameters
  • expr (str) The query string to evaluate.
    You can refer to variables in the environment by prefixing them with an '@' character like @a + b.
    You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named "Area (cm^2)" would be referenced as Area (cm^2)). Column names which are Python keywords (like "list", "for", "import", etc) cannot be used.
    For example, if one of your columns is called a a and you want to sum it with b, your query should be `a a` + b.
    .. versionadded:: 0.25.0 Backtick quoting introduced.
    .. versionadded:: 1.0.0 Expanding functionality of backtick quoting for more than only spaces.
  • inplace (bool) Whether the query should modify the data in place or returna modified copy.
  • **kwargs See the documentation for :func:eval for complete detailson the keyword arguments accepted by :meth:DataFrame.query.
Returns (DataFrame or None)

DataFrame resulting from the provided query expression orNone if inplace=True.

See Also

eval : Evaluate a string describing operations on DataFrame columns. DataFrame.eval : Evaluate a string describing operations on DataFrame columns.

Notes

The result of the evaluation of this expression is first passed to :attr:DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to :meth:DataFrame.__getitem__.

This method uses the top-level :func:eval function to evaluate the passed query.

The :meth:~pandas.DataFrame.query method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, :keyword:and and :keyword:or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The :attr:DataFrame.index and :attr:DataFrame.columns attributes of the :class:~pandas.DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.

For further details and examples see the query documentation in :ref:indexing <indexing.query>.

Backtick quoted variables

Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.

During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped).

In a special case, quotes that make a pair around a backtick can confuse the parser. For example, it's` > `that's will raise an error, as it forms a quoted string ('s > `that') with a backtick inside.

See also the Python documentation about lexical analysis (https://docs.python.org/3/reference/lexical_analysis.html) in combination with the source code in :mod:pandas.core.computation.parsing.

Examples
>>> df = pd.DataFrame({'A': range(1, 6),...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
   A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.query('A > B')
   A  B  C C
4  5  2    6

The previous expression is equivalent to

>>> df[df.A > df.B]
   A  B  C C
4  5  2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
   A   B  C C
0  1  10   10

The previous expression is equivalent to

>>> df[df.B == df['C C']]
   A   B  C C
0  1  10   10
method

eval(expr, inplace=False, **kwargs)

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters
  • expr (str) The expression string to evaluate.
  • inplace (bool, default False) If the expression contains an assignment, whether to perform theoperation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
  • **kwargs See the documentation for :func:eval for complete detailson the keyword arguments accepted by :meth:~pandas.DataFrame.query.
Returns (ndarray, scalar, pandas object, or None)

The result of the evaluation or None if inplace=True.

See Also

DataFrame.query : Evaluates a boolean expression to query the columns of a frame. DataFrame.assign : Can evaluate an expression or function to create new values for a column. eval : Evaluate a Python expression as a string using various backends.

Notes

For more details see the API documentation for :func:~eval. For detailed examples see :ref:enhancing performance with eval <enhancingperf.eval>.

Examples
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2

Use inplace=True to modify the original DataFrame.

>>> df.eval('C = A + B', inplace=True)
>>> df
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3
method

select_dtypes(include=None, exclude=None)

Return a subset of the DataFrame's columns based on the column dtypes.

Returns (DataFrame)

The subset of the frame including the dtypes in include andexcluding the dtypes in exclude.

Raises
  • ValueError
    • If both of include and exclude are empty
    • If include and exclude have overlapping elements
    • If any kind of string dtype is passed in.
See Also

DataFrame.dtypes: Return Series with the data type of each column.

Notes

  • To select all numeric types, use np.number or 'number'
  • To select strings you must use the object dtype, but note that this will return all object dtype columns
  • See the numpy dtype hierarchy <https://numpy.org/doc/stable/reference/arrays.scalars.html>__
  • To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
  • To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'
  • To select Pandas categorical dtypes, use 'category'
  • To select Pandas datetimetz dtypes, use 'datetimetz' (new in 0.20.0) or 'datetime64[ns, tz]'
Examples
>>> df = pd.DataFrame({'a': [1, 2] * 3,...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0
>>> df.select_dtypes(include='bool')
   b
0  True
1  False
2  True
3  False
4  True
5  False
>>> df.select_dtypes(include=['float64'])
   c
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0
>>> df.select_dtypes(exclude=['int64'])
       b    c
0   True  1.0
1  False  2.0
2   True  1.0
3  False  2.0
4   True  1.0
5  False  2.0
method

insert(loc, column, value, allow_duplicates=False)

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

Parameters
  • loc (int) Insertion index. Must verify 0 <= loc <= len(columns).
  • column (str, number, or hashable object) Label of the inserted column.
See Also

Index.insert : Insert new item by index.

Examples
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})>>> df
   col1  col2
0     1     3
1     2     4
>>> df.insert(1, "newcol", [99, 99])
>>> df
   col1  newcol  col2
0     1      99     3
1     2      99     4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
   col1  col1  newcol  col2
0   100     1      99     3
1   100     2      99     4

Notice that pandas uses index alignment in case of value from type Series:

>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
   col0  col1  col1  newcol  col2
0   NaN   100     1      99     3
1   5.0   100     2      99     4
method

assign(**kwargs)

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters
  • **kwargs (dict of {str: callable or Series}) The column names are keywords. If the values arecallable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn't check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
Returns (DataFrame)

A new DataFrame with the new columns in addition toall the existing columns.

Notes

Assigning multiple columns within the same assign is possible. Later items in '**kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order.

Examples
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15
method

lookup(row_labels, col_labels)

Label-based "fancy indexing" function for DataFrame.Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair.

.. deprecated:: 1.2.0 DataFrame.lookup is deprecated, use DataFrame.melt and DataFrame.loc instead. For further details see :ref:Looking up values by index/column labels <indexing.lookup>.

Parameters
  • row_labels (sequence) The row labels to use for lookup.
  • col_labels (sequence) The column labels to use for lookup.
Returns (numpy.ndarray)

The found values.

method

align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters
  • axis (allowed axis of the other object, default None) Align on index (0), columns (1), or both (None).
  • level (int or level name, default None) Broadcast across a level, matching Index values on thepassed MultiIndex level.
  • copy (bool, default True) Always returns new objects. If copy=False and no reindexing isrequired then original objects are returned.
  • fill_value (scalar, default np.NaN) Value to use for missing values. Defaults to NaN, but can be any"compatible" value.
  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) Method to use for filling holes in reindexed Series:
    • pad / ffill: propagate last valid observation forward to next valid.
    • backfill / bfill: use NEXT valid observation to fill gap.
  • limit (int, default None) If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
  • fill_axis ({0 or 'index', 1 or 'columns'}, default 0) Filling axis, method and limit.
  • broadcast_axis ({0 or 'index', 1 or 'columns'}, default None) Broadcast values along this axis, if aligning two objects ofdifferent dimensions.
Returns (left, right) : (DataFrame, type of other)

Aligned objects.

method

set_axis(labels, axis=0, inplace=False)

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or Index.

Parameters
  • labels (list-like, Index) The values for the new index.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to update. The value 0 identifies the rows, and 1 identifies the columns.
  • inplace (bool, default False) Whether to return a new DataFrame instance.
Returns (renamed : DataFrame or None)

An object of type DataFrame or None if inplace=True.

See Also

DataFrame.renameaxis : Alter the name of the index or columns.

    Examples
    --------

~~~python

df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) ~~~

    Change the row labels.
>>> df.set_axis(['a', 'b', 'c'], axis='index')
   A  B
a  1  4
b  2  5
c  3  6
    Change the column labels.
>>> df.set_axis(['I', 'II'], axis='columns')
   I  II
0  1   4
1  2   5
2  3   6
    Now, update the labels inplace.
>>> df.set_axis(['i', 'ii'], axis='columns', inplace=True)
>>> df
   i  ii
0  1   4
1  2   5
2  3   6
method

drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide <advanced.shown_levels> for more information about the now unused levels.

Parameters
  • labels (single label or list-like) Index or column labels to drop.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Whether to drop labels from the index (0 or 'index') orcolumns (1 or 'columns').
  • index (single label or list-like) Alternative to specifying axis (labels, axis=0is equivalent to index=labels).
  • columns (single label or list-like) Alternative to specifying axis (labels, axis=1is equivalent to columns=labels).
  • level (int or level name, optional) For MultiIndex, level from which the labels will be removed.
  • inplace (bool, default False) If False, return a copy. Otherwise, do operationinplace and return None.
  • errors ({'ignore', 'raise'}, default 'raise') If 'ignore', suppress error and only existing labels aredropped.
Returns (DataFrame or None)

DataFrame without the removed index or column labels orNone if inplace=True.

Raises
  • KeyError If any of the labels is not found in the selected axis.
See Also

DataFrame.loc : Label-location based indexer for selection by label.DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. DataFrame.dropduplicates : Return DataFrame with duplicate rows removed, optionally only considering certain columns. Series.drop : Return Series with specified index labels removed.

Examples
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2
>>> df.drop(index='cow', columns='small')
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3
>>> df.drop(index='length', level=1)
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8
method

rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore')

Alter axes labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error.

See the :ref:user guide <basics.rename> for more.

Parameters
  • mapper (dict-like or function) Dict-like or function transformations to apply tothat axis' values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.
  • index (dict-like or function) Alternative to specifying axis (mapper, axis=0is equivalent to index=mapper).
  • columns (dict-like or function) Alternative to specifying axis (mapper, axis=1is equivalent to columns=mapper).
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis to target with mapper. Can be either the axis name('index', 'columns') or number (0, 1). The default is 'index'.
  • copy (bool, default True) Also copy underlying data.
  • inplace (bool, default False) Whether to return a new DataFrame. If True then value of copy isignored.
  • level (int or level name, default None) In case of a MultiIndex, only rename labels in the specifiedlevel.
  • errors ({'ignore', 'raise'}, default 'ignore') If 'raise', raise a KeyError when a dict-like mapper, index,or columns contains labels that are not present in the Index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored.
Returns (DataFrame or None)

DataFrame with the renamed axis labels or None if inplace=True.

Raises
  • KeyError If any of the labels is not found in the selected axis and"errors='raise'".
See Also

DataFrame.renameaxis : Set the name of the axis.

Examples

DataFrame.rename supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)
  • (mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters:

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6
>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6
method

fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)

Fill NA/NaN values using the specified method.

Parameters
  • value (scalar, dict, Series, or DataFrame) Value to use to fill holes (e.g. 0), alternately adict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.
  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) Method to use for filling holes in reindexed Seriespad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap.
  • axis ({0 or 'index', 1 or 'columns'}) Axis along which to fill missing values.
  • inplace (bool, default False) If True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in a DataFrame).
  • limit (int, default None) If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
  • downcast (dict, default is None) A dict of item->dtype of what to downcast if possible,or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).
Returns (DataFrame or None)

Object with missing values filled or None if inplace=True.

See Also

interpolate : Fill NaN values using interpolation.reindex : Conform object to new index. asfreq : Convert TimeSeries to specified frequency.

Examples
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, 5],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

Replace all NaN elements with 0s.

>>> df.fillna(0)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4

We can also propagate non-null values forward or backward.

>>> df.fillna(method="ffill")
    A   B   C   D
0   NaN 2.0 NaN 0
1   3.0 4.0 NaN 1
2   3.0 4.0 NaN 5
3   3.0 3.0 NaN 4

Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 2.0 1
2   0.0 1.0 2.0 5
3   0.0 3.0 2.0 4

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 NaN 1
2   NaN 1.0 NaN 5
3   NaN 3.0 NaN 4

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4
method

pop(item)

Return item and drop from frame. Raise KeyError if not found.

Parameters
  • item (label) Label of column to be popped.
Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN
>>> df.pop('class')
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object
>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN
method

replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')

Replace values given in to_replace with value.

Values of the DataFrame are replaced with other values dynamically.

This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Parameters
  • to_replace (str, regex, list, dict, Series, int, float, or None) How to find the values that will be replaced.
    • numeric, str or regex:
      • numeric: numeric values equal to to_replace will be replaced with value
      • str: string exactly matching to_replace will be replaced with value
      • regex: regexs matching to_replace will be replaced with value
    • list of str, regex, or numeric:
      • First, if to_replace and value are both lists, they must be the same length.
      • Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for value since there are only a few possible substitution regexes you can use.
      • str, regex and numeric rules apply as above.
    • dict:
      • Dicts can be used to specify different replacement values for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value 'a' with 'b' and 'y' with 'z'. To use a dict in this way the value parameter should be None.
      • For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column 'a' and the value 'z' in column 'b' and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.
      • For a DataFrame nested dictionaries, e.g., {'a': {'b': np.nan}}, are read as follows: look in column 'a' for the value 'b' and replace it with NaN. The value parameter should be None to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
    • None:
      • This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.
    See the examples section for examples of each of these.
  • value (scalar, dict, list, str, regex, default None) Value to replace any values matching to_replace with.For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.
  • inplace (bool, default False) If True, performs operation inplace and returns None.
  • limit (int, default None) Maximum size gap to forward or backward fill.
  • regex (bool or same types as `to_replace`, default False) Whether to interpret to_replace and/or value as regularexpressions. If this is True then to_replace must be a string. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.
  • method ({'pad', 'ffill', 'bfill', `None`}) The method to use when for replacement, when to_replace is ascalar, list or tuple and value is None.
    .. versionchanged:: 0.23.0 Added to DataFrame.
Returns (DataFrame)

Object after replacement.

Raises
  • AssertionError
    • If regex is not a bool and to_replace is not None.
  • TypeError
    • If to_replace is not a scalar, array-like, dict, or None
    • If to_replace is a dict and value is not a list, dict, ndarray, or Series
    • If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series.
    • When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced
  • ValueError
    • If a list or an ndarray is passed to to_replace and value but they are not the same length.
See Also

DataFrame.fillna : Fill NA values.DataFrame.where : Replace values based on boolean condition. Series.str.replace : Simple string replacement.

Notes

  • Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
  • Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
  • This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.
  • When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.
Examples

*

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) C a b c d e

) C a b c d e

) 0 3 3 3 4 4

*

) C a b c d e

) C a b c d e

) C a b c d e

*

, ) ) B c w z

) B c r z

) B c w z

) B c w z

) B c w z

d s :

)

e . o :

) 0 e e b e t

r e 0 . o :

) 0 0 0 b b t

method

shift(periods=1, freq=None, axis=0, fill_value=<no_default>)

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as "infer" as long as either freq or inferred_freq attribute is set in the index.

Parameters
  • periods (int) Number of periods to shift. Can be positive or negative.
  • freq (DateOffset, tseries.offsets, timedelta, or str, optional) Offset to use from the tseries module or time rule (e.g. 'EOM').If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as "infer" then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
  • axis ({0 or 'index', 1 or 'columns', None}, default None) Shift direction.
  • fill_value (object, optional) The scalar value to use for newly introduced missing values.the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. :attr:NaT is used. For extension dtypes, self.dtype.na_value is used.
    .. versionchanged:: 1.1.0
Returns (DataFrame)

Copy of input object, shifted.

See Also

Index.shift : Shift values of Index.DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex. tshift : Shift the time index, using the index's frequency if available.

Examples
>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],...                    "Col2": [13, 23, 18, 33, 48],
...                    "Col3": [17, 27, 22, 37, 52]},
...                   index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52
>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0
>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01   NaN    10    13
2020-01-02   NaN    20    23
2020-01-03   NaN    15    18
2020-01-04   NaN    30    33
2020-01-05   NaN    45    48
>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27
>>> df.shift(periods=3, freq="D")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
>>> df.shift(periods=3, freq="infer")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
method

set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.

Parameters
  • keys (label or array-like or list of labels/arrays) This parameter can be either a single column key, a single array ofthe same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, "array" encompasses :class:Series, :class:Index, np.ndarray, and instances of :class:~collections.abc.Iterator.
  • drop (bool, default True) Delete columns to be used as the new index.
  • append (bool, default False) Whether to append columns to existing index.
  • inplace (bool, default False) If True, modifies the DataFrame in place (do not create a new object).
  • verify_integrity (bool, default False) Check the new index for duplicates. Otherwise defer the check untilnecessary. Setting to False will improve the performance of this method.
Returns (DataFrame or None)

Changed row labels or None if inplace=True.

See Also

DataFrame.reset_index : Opposite of set_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.

Examples
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],...                    'year': [2012, 2014, 2013, 2014],
...                    'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31

Set the index to become the 'month' column:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31

Create a MultiIndex using columns 'year' and 'month':

>>> df.set_index(['year', 'month'])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a MultiIndex using an Index and a column:

>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
      month  year  sale
1 1       1  2012    55
2 4       4  2014    40
3 9       7  2013    84
4 16     10  2014    31
method

reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')

Reset the index, or a level of it.

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters
  • level (int, str, tuple, or list, default None) Only remove the given levels from the index. Removes all levels bydefault.
  • drop (bool, default False) Do not try to insert index into dataframe columns. This resetsthe index to the default integer index.
  • inplace (bool, default False) Modify the DataFrame in place (do not create a new object).
  • col_level (int or str, default 0) If the columns have multiple levels, determines which level thelabels are inserted into. By default it is inserted into the first level.
  • col_fill (object, default '') If the columns have multiple levels, determines how the otherlevels are named. If None then the index name is repeated.
Returns (DataFrame or None)

DataFrame with the new index or None if inplace=True.

See Also

DataFrame.set_index : Opposite of reset_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.

Examples
>>> df = pd.DataFrame([('bird', 389.0),...                    ('bird', 24.0),
...                    ('mammal', 80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
...                    ( 24.0, 'fly'),
...                    ( 80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump
method

isna()

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.

See Also

DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
method

isnull()

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.

See Also

DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
method

notna()

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or :attr:numpy.NaN, get mapped to False values.

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.

See Also

DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
method

notnull()

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or :attr:numpy.NaN, get mapped to False values.

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.

See Also

DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
method

dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Remove missing values.

See the :ref:User Guide <missing_data> for more on which values are considered missing, and how to work with missing data.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Determine if rows or columns which contain missing values areremoved.
    • 0, or 'index' : Drop rows which contain missing values.
    • 1, or 'columns' : Drop columns which contain missing value.
    .. versionchanged:: 1.0.0
    Pass tuple or list to drop on multiple axes. Only a single axis is allowed.
  • how ({'any', 'all'}, default 'any') Determine if row or column is removed from DataFrame, when we haveat least one NA or all NA.
    • 'any' : If any NA values are present, drop that row or column.
    • 'all' : If all values are NA, drop that row or column.
  • thresh (int, optional) Require that many non-NA values.
  • subset (array-like, optional) Labels along other axis to consider, e.g. if you are dropping rowsthese would be a list of columns to include.
  • inplace (bool, default False) If True, do operation inplace and return None.
Returns (DataFrame or None)

DataFrame with NA entries dropped from it or None if inplace=True.

See Also

DataFrame.isna: Indicate missing values.DataFrame.notna : Indicate existing (non-missing) values. DataFrame.fillna : Replace missing values. Series.dropna : Drop missing values. Index.dropna : Drop missing indices.

Examples
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep the DataFrame with valid entries in the same variable.

>>> df.dropna(inplace=True)
>>> df
     name        toy       born
1  Batman  Batmobile 1940-04-25
method

drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False)

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters
  • subset (column label or sequence of labels, optional) Only consider certain columns for identifying duplicates, bydefault use all of the columns.
  • keep ({'first', 'last', False}, default 'first') Determines which duplicates (if any) to keep.
    • - first : Drop duplicates except for the first occurrence.
    • - last : Drop duplicates except for the last occurrence.
    • - False : Drop all duplicates.
  • inplace (bool, default False) Whether to drop duplicates in place or to return a copy.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
Returns (DataFrame or None)

DataFrame with duplicates removed or None if inplace=True.

See Also

DataFrame.value_counts: Count unique combinations of columns.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0
method

duplicated(subset=None, keep='first')

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters
  • subset (column label or sequence of labels, optional) Only consider certain columns for identifying duplicates, bydefault use all of the columns.
  • keep ({'first', 'last', False}, default 'first') Determines which duplicates (if any) to mark.
    • first : Mark duplicates as True except for the first occurrence.
    • last : Mark duplicates as True except for the last occurrence.
    • False : Mark all duplicates as True.
Returns (Series)

Boolean series for each duplicated rows.

See Also

Index.duplicated : Equivalent method on index.Series.duplicated : Equivalent method on Series. Series.dropduplicates : Remove duplicate values from Series. DataFrame.dropduplicates : Remove duplicate values from DataFrame.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, for each set of duplicated values, the first occurrence is set on False and all others on True.

>>> df.duplicated()
0    False
1     True
2    False
3    False
4    False
dtype: bool

By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True.

>>> df.duplicated(keep='last')
0     True
1    False
2    False
3    False
4    False
dtype: bool

By setting keep on False, all duplicates are True.

>>> df.duplicated(keep=False)
0     True
1     True
2    False
3    False
4    False
dtype: bool

To find duplicates on specific column(s), use subset.

>>> df.duplicated(subset=['brand'])
0    False
1     True
2    False
3     True
4     True
dtype: bool
method

sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

Sort by the values along either axis.

Parameters
  • axis (str or int, optional) Axis to be sorted.
  • ascending (optional) Sort ascending vs. descending. Specify list for multiple sortorders. If this is a list of bools, must match the length of the by.
  • inplace (bool, optional) If True, perform operation in-place.
  • kind (str, optional) Choice of sorting algorithm. See also :func:numpy.sort for moreinformation. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
  • na_position (str, optional) Puts NaNs at the beginning if first; last puts NaNs at theend.
  • ignore_index (bool, optional) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
  • key (callable(Series: Series, ExtensionArray, ndarray, or Index), optional) Apply the key function to the valuesbefore sorting. This is similar to the key argument in the builtin :meth:sorted function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently.
    .. versionadded:: 1.1.0
  • by (str or list of str) Name or list of names to sort by.
    • if axis is 0 or 'index' then by may contain index levels and/or column labels.
    • if axis is 1 or 'columns' then by may contain column levels and/or index labels.
Returns (DataFrame or None)

DataFrame with sorted values or None if inplace=True.

See Also

DataFrame.sort_index : Sort a DataFrame by the index.Series.sort_values : Similar method for a Series.

Examples
>>> df = pd.DataFrame({...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Sort by col1

>>> df.sort_values(by=['col1'])
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
  col1  col2  col3 col4
1    A     1     1    B
0    A     2     0    a
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
  col1  col2  col3 col4
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B
3  NaN     8     4    D

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
  col1  col2  col3 col4
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B

Sorting with a key function

>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
   col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Natural sort with the key argument, using the natsort <https://github.com/SethMMorton/natsort> package.

>>> df = pd.DataFrame({
...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
...    "value": [10, 20, 30, 40, 50]
... })
>>> df
    time  value
0    0hr     10
1  128hr     20
2   72hr     30
3   48hr     40
4   96hr     50
>>> from natsort import index_natsorted
>>> df.sort_values(
...    by="time",
...    key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
    time  value
0    0hr     10
3   48hr     40
2   72hr     30
4   96hr     50
1  128hr     20
method

sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis along which to sort. The value 0 identifies the rows,and 1 identifies the columns.
  • level (int or level name or list of ints or list of level names) If not None, sort on values in specified index level(s).
  • ascending (bool or list-like of bools, default True) Sort ascending vs. descending. When the index is a MultiIndex thesort direction can be controlled for each level individually.
  • inplace (bool, default False) If True, perform operation in-place.
  • kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') Choice of sorting algorithm. See also :func:numpy.sort for moreinformation. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
  • na_position ({'first', 'last'}, default 'last') Puts NaNs at the beginning if first; last puts NaNs at the end.Not implemented for MultiIndex.
  • sort_remaining (bool, default True) If True and sorting by level and index is multilevel, sort by otherlevels too (in order) after sorting by specified level.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
  • key (callable, optional) If not None, apply the key function to the index valuesbefore sorting. This is similar to the key argument in the builtin :meth:sorted function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.
    .. versionadded:: 1.1.0
Returns (DataFrame or None)

The original DataFrame sorted by the labels or None if inplace=True.

See Also

Series.sort_index : Sort Series by the index.DataFrame.sort_values : Sort DataFrame by the value. Series.sort_values : Sort Series by the value.

Examples
>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],...                   columns=['A'])
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

>>> df.sort_index(ascending=False)
     A
234  3
150  5
100  1
29   2
1    4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4
method

value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)

Return a Series containing counts of unique rows in the DataFrame.

.. versionadded:: 1.1.0

Parameters
  • subset (list-like, optional) Columns to use when counting unique combinations.
  • normalize (bool, default False) Return proportions rather than frequencies.
  • sort (bool, default True) Sort by frequencies.
  • ascending (bool, default False) Sort in ascending order.
  • dropna (bool, default True) Don’t include counts of rows that contain NA values.
    .. versionadded:: 1.3.0
See Also

Series.value_counts: Equivalent method on Series.

Notes

The returned Series will have a MultiIndex with one level per input column. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],...                    'num_wings': [2, 0, 0, 0]},
...                   index=['falcon', 'dog', 'cat', 'ant'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0
>>> df.value_counts()
num_legs  num_wings
4         0            2
2         2            1
6         0            1
dtype: int64
>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
dtype: int64
>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
dtype: int64
>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
2         2            0.25
6         0            0.25
dtype: float64

With dropna set to False we can also count rows with NA values.

>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
>>> df
  first_name middle_name
0       John       Smith
1       Anne        <NA>
2       John        <NA>
3       Beth      Louise
>>> df.value_counts()
first_name  middle_name
Beth        Louise         1
John        Smith          1
dtype: int64
>>> df.value_counts(dropna=False)
first_name  middle_name
Anne        NaN            1
Beth        Louise         1
John        Smith          1
            NaN            1
dtype: int64
method

nlargest(n, columns, keep='first')

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters
  • n (int) Number of rows to return.
  • columns (label or list of labels) Column label(s) to order by.
  • keep ({'first', 'last', 'all'}, default 'first') Where there are duplicate values:
    • first : prioritize the first occurrence(s)
    • last : prioritize the last occurrence(s)
    • all : do not drop any duplicates, even it means selecting more than n items.
Returns (DataFrame)

The first n rows ordered by the given columns in descendingorder.

See Also

DataFrame.nsmallest : Return the first n rows ordered by columns in ascending order. DataFrame.sort_values : Sort DataFrame by the values. DataFrame.head : Return the first n rows without re-ordering.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,...                                   434000, 434000, 337000, 11300,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column "population".

>>> df.nlargest(3, 'population')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'population', keep='last')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', all duplicate items are maintained:

>>> df.nlargest(3, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column "population" and then "GDP", we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['population', 'GDP'])
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN
method

nsmallest(n, columns, keep='first')

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters
  • n (int) Number of items to retrieve.
  • columns (list or str) Column name or names to order by.
  • keep ({'first', 'last', 'all'}, default 'first') Where there are duplicate values:
    • first : take the first occurrence.
    • last : take the last occurrence.
    • all : do not drop any duplicates, even it means selecting more than n items.
See Also

DataFrame.nlargest : Return the first n rows ordered by columns in descending order. DataFrame.sort_values : Sort DataFrame by the values. DataFrame.head : Return the first n rows without re-ordering.

Examples
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,...                                   434000, 434000, 337000, 337000,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column "population".

>>> df.nsmallest(3, 'population')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, 'population', keep='last')
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', all duplicate items are maintained:

>>> df.nsmallest(3, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column "population" and then "GDP", we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ['population', 'GDP'])
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR
method

swaplevel(i=-2, j=-1, axis=0)

Swap levels i and j in a :class:MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to swap levels on. 0 or 'index' for row-wise, 1 or'columns' for column-wise.
Returns (DataFrame)

DataFrame with levels swapped in MultiIndex.

Examples
>>> df = pd.DataFrame(...     {"Grade": ["A", "B", "A", "C"]},
...     index=[
...         ["Final exam", "Final exam", "Coursework", "Coursework"],
...         ["History", "Geography", "History", "Geography"],
...         ["January", "February", "March", "April"],
...     ],
... )
>>> df
                                    Grade
Final exam  History     January      A
            Geography   February     B
Coursework  History     March        A
            Geography   April        C

In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.

>>> df.swaplevel()
                                    Grade
Final exam  January     History         A
            February    Geography       B
Coursework  March       History         A
            April       Geography       C

By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.

>>> df.swaplevel(0)
                                    Grade
January     History     Final exam      A
February    Geography   Final exam      B
March       History     Coursework      A
April       Geography   Coursework      C

We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.

>>> df.swaplevel(0, 1)
                                    Grade
History     Final exam  January         A
Geography   Final exam  February        B
History     Coursework  March           A
Geography   Coursework  April           C
method

reorder_levels(order, axis=0)

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters
  • order (list of int or list of str) List representing new level order. Reference level by number(position) or by key (label).
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Where to reorder levels.
method

compare(other, align_axis=1, keep_shape=False, keep_equal=False)

Compare to another DataFrame and show the differences.

.. versionadded:: 1.1.0

Parameters
  • other (DataFrame) Object to compare with.
  • align_axis ({0 or 'index', 1 or 'columns'}, default 1) Determine which axis to align the comparison on.
    • 0, or 'index' : Resulting differences are stacked vertically with rows drawn alternately from self and other.
    • 1, or 'columns' : Resulting differences are aligned horizontally with columns drawn alternately from self and other.
  • keep_shape (bool, default False) If true, all rows and columns are kept.Otherwise, only the ones with different values are kept.
  • keep_equal (bool, default False) If true, the result keeps values that are equal.Otherwise, equal values are shown as NaNs.
Returns (DataFrame)

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with 'self' and 'other' stacked alternately at the inner level.

Raises
  • ValueError When the two DataFrames don't have identical labels or shape.
See Also

Series.compare : Compare with another Series and show differences.DataFrame.equals : Test whether two objects contain the same elements.

Notes

Matching NaNs will not appear as a difference.

Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames

Examples
>>> df = pd.DataFrame(...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0
>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Align the differences on columns

>>> df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0
method

combine(other, func, fill_value=None, overwrite=True)

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters
  • other (DataFrame) The DataFrame to merge column-wise.
  • func (function) Function that takes two series as inputs and return a Series or ascalar. Used to merge the two dataframes column by columns.
  • fill_value (scalar value, default None) The value to fill NaNs with prior to passing any column to themerge func.
  • overwrite (bool, default True) If True, columns in self that do not exist in other will beoverwritten with NaNs.
Returns (DataFrame)

Combination of the provided DataFrames.

See Also

DataFrame.combinefirst : Combine two DataFrame objects and default to non-null values in frame calling the method.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
   A    B
0  0 -5.0
1  0  4.0

However, if the same element in both dataframes is None, that None is preserved

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
    A    B
0  0 -5.0
1  0  3.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
   A    B   C
0  0.0  NaN NaN
1  0.0  3.0 NaN
2  NaN  3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
     A    B   C
0  0.0  NaN NaN
1  0.0  3.0 1.0
2  NaN  3.0 1.0
method

combine_first(other)

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters
  • other (DataFrame) Provided DataFrame to use to fill null values.
Returns (DataFrame)

The result of combining the provided DataFrame with the other object.

See Also

DataFrame.combine : Perform series-wise operation on two DataFrames using a given function.

Examples
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0
method

update(other, join='left', overwrite=True, filter_func=None, errors='ignore')

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters
  • other (DataFrame, or object coercible into a DataFrame) Should have at least one matching index/column labelwith the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.
  • join ({'left'}, default 'left') Only left join is implemented, keeping the index and columns of theoriginal object.
  • overwrite (bool, default True) How to handle non-NA values for overlapping keys:
    • True: overwrite original DataFrame's values with values from other.
    • False: only update values that are NA in the original DataFrame.
  • filter_func (callable(1d-array) -> bool 1d-array, optional) Can choose to replace values other than NA. Return True for valuesthat should be updated.
  • errors ({'raise', 'ignore'}, default 'ignore') If 'raise', will raise a ValueError if the DataFrame and otherboth contain non-NA data in the same place.
Raises
  • NotImplementedError
    • If join != 'left'
  • ValueError
    • When errors='raise' and there's overlapping non-NA data.
    • When errors is not either 'ignore' or 'raise'
See Also

dict.update : Similar method for dictionaries.DataFrame.merge : For column(s)-on-column(s) operations.

Examples
>>> df = pd.DataFrame({'A': [1, 2, 3],...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame's length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

For Series, its name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  y
2  c  e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  x
1  b  d
2  c  e

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0
method

groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=<no_default>, observed=False, dropna=True)

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters
  • by (mapping, function, label, or list of labels) Used to determine the groups for the groupby.If by is a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see .align() method). If an ndarray is passed, the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Split along rows (0) or columns (1).
  • level (int, level name, or sequence of such, default None) If the axis is a MultiIndex (hierarchical), group by a particularlevel or levels.
  • as_index (bool, default True) For aggregated output, return object with group labels as theindex. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output.
  • sort (bool, default True) Sort group keys. Get better performance by turning this off.Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.
  • group_keys (bool, default True) When calling apply, add group keys to index to identify pieces.
  • squeeze (bool, default False) Reduce the dimensionality of the return type if possible,otherwise return a consistent type.
    .. deprecated:: 1.1.0
  • observed (bool, default False) This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
  • dropna (bool, default True) If True, and if group keys contain NA values, NA values togetherwith row/column will be dropped. If False, NA values will also be treated as the key in groups
    .. versionadded:: 1.1.0
Returns (DataFrameGroupBy)

Returns a groupby object that contains information about the groups.

See Also

resample : Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide <https://pandas.pydata.org/pandas-docs/stable/groupby.html>__ for more.

Examples
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True:

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5
>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4
>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0
method

pivot(index=None, columns=None, values=None)

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the :ref:User Guide <reshaping> for more on reshaping.

Parameters
  • index (str or object or a list of str, optional) Column to use to make new frame's index. If None, usesexisting index.
    .. versionchanged:: 1.1.0 Also accept list of index names.
  • columns (str or object or a list of str) Column to use to make new frame's columns.
    .. versionchanged:: 1.1.0 Also accept list of columns names.
  • values (str, object or a list of the previous, optional) Column(s) to use for populating new frame's values. If notspecified, all remaining columns will be used and the result will have hierarchically indexed columns.
Returns (DataFrame)

Returns reshaped DataFrame.

Raises
  • ValueError When there are any index, columns combinations with multiplevalues. DataFrame.pivot_table when you need to aggregate.
See Also

DataFrame.pivottable : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.

Notes

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

Examples
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',...                            'two'],
...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
...                    'baz': [1, 2, 3, 4, 5, 6],
...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
    foo   bar  baz  zoo
0   one   A    1    x
1   one   B    2    y
2   one   C    3    z
3   two   A    4    q
4   two   B    5    w
5   two   C    6    t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar')['baz']
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>> df = pd.DataFrame({
...        "lev1": [1, 1, 1, 2, 2, 2],
...        "lev2": [1, 1, 2, 1, 1, 2],
...        "lev3": [1, 2, 1, 2, 1, 2],
...        "lev4": [1, 2, 3, 4, 5, 6],
...        "values": [0, 1, 2, 3, 4, 5]})
>>> df
    lev1 lev2 lev3 lev4 values
0   1    1    1    1    0
1   1    1    2    2    1
2   1    2    1    3    2
3   2    1    2    4    3
4   2    1    1    5    4
5   2    2    2    6    5
>>> df.pivot(index="lev1", columns=["lev2", "lev3"],values="values")
lev2    1         2
lev3    1    2    1    2
lev1
1     0.0  1.0  2.0  NaN
2     4.0  3.0  NaN  5.0
>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
      lev3    1    2
lev1  lev2
   1     1  0.0  1.0
         2  2.0  NaN
   2     1  4.0  3.0
         2  NaN  5.0

A ValueError is raised if there are any duplicates.

>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
...                    "bar": ['A', 'A', 'B', 'C'],
...                    "baz": [1, 2, 3, 4]})
>>> df
   foo bar  baz
0  one   A    1
1  one   A    2
2  two   B    3
3  two   C    4

Notice that the first two rows are the same for our index and columns arguments.

>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
   ...
ValueError: Index contains duplicate entries, cannot reshape
method

pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True)

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters
  • index (column, Grouper, array, or list of the previous) If an array is passed, it must be the same length as the data. Thelist can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.
  • columns (column, Grouper, array, or list of the previous) If an array is passed, it must be the same length as the data. Thelist can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.
  • aggfunc (function, list of functions, dict, default numpy.mean) If list of functions passed, the resulting pivot table will havehierarchical columns whose top level are the function names (inferred from the function objects themselves) If dict is passed, the key is column to aggregate and value is function or list of functions.
  • fill_value (scalar, default None) Value to replace missing values with (in the resulting pivot table,after aggregation).
  • margins (bool, default False) Add all row / columns (e.g. for subtotal / grand totals).
  • dropna (bool, default True) Do not include columns whose entries are all NaN.
  • margins_name (str, default 'All') Name of the row / column that will contain the totalswhen margins is True.
  • observed (bool, default False) This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
    .. versionchanged:: 0.25.0
  • sort (bool, default True) Specifies if the result should be sorted.
    .. versionadded:: 1.3.0
Returns (DataFrame)

An Excel style pivot table.

See Also

DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.melt: Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.

Examples
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9

This first example aggregates values by taking the sum.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum)
>>> table
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

We can also fill missing values using the fill_value parameter.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C        large  small
A   B
bar one      4      5
    two      7      6
foo one      4      1
    two      0      6

The next example aggregates by taking the mean across multiple columns.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': np.mean})
>>> table
                D         E
A   C
bar large  5.500000  7.500000
    small  5.500000  8.500000
foo large  2.000000  4.500000
    small  2.333333  4.333333

We can also calculate multiple types of aggregations for any given value column.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': [min, max, np.mean]})
>>> table
                D    E
            mean  max      mean  min
A   C
bar large  5.500000  9.0  7.500000  6.0
    small  5.500000  9.0  8.500000  8.0
foo large  2.000000  5.0  4.500000  4.0
    small  2.333333  6.0  4.333333  2.0
method

stack(level=-1, dropna=True)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

  • if the columns have a single level, the output is a Series;
  • if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.
Parameters
  • level (int, str, list, default -1) Level(s) to stack from the column axis onto the indexaxis, defined as one index or label, or a list of indices or labels.
  • dropna (bool, default True) Whether to drop rows in the resulting Frame/Series withmissing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.
Returns (DataFrame or Series)

Stacked dataframe or series.

See Also

DataFrame.unstack : Unstack prescribed level(s) from index axis onto column axis. DataFrame.pivot : Reshape dataframe from long format to wide format. DataFrame.pivottable : Create a spreadsheet-style pivot table as a DataFrame.

Notes

The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).

Examples

Single level columns

>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
...                                     index=['cat', 'dog'],
...                                     columns=['weight', 'height'])

Stacking a dataframe with a single level column axis returns a Series:

>>> df_single_level_cols
     weight height
cat       0      1
dog       2      3
>>> df_single_level_cols.stack()
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64

Multi level columns: simple case

>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol1)

Stacking a dataframe with a multi-level column axis:

>>> df_multi_level_cols1
     weight
         kg    pounds
cat       1        2
dog       2        4
>>> df_multi_level_cols1.stack()
            weight
cat kg           1
    pounds       2
dog kg           2
    pounds       4

Missing values

>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:

>>> df_multi_level_cols2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0
>>> df_multi_level_cols2.stack()
        height  weight
cat kg     NaN     1.0
    m      2.0     NaN
dog kg     NaN     3.0
    m      4.0     NaN

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0)
             kg    m
cat height  NaN  2.0
    weight  1.0  NaN
dog height  NaN  4.0
    weight  3.0  NaN
>>> df_multi_level_cols2.stack([0, 1])
cat  height  m     2.0
     weight  kg    1.0
dog  height  m     4.0
     weight  kg    3.0
dtype: float64

Dropping missing values

>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

Note that rows where all values are missing are dropped by default but this behaviour can be controlled via the dropna keyword parameter:

>>> df_multi_level_cols3
    weight height
        kg      m
cat    NaN    1.0
dog    2.0    3.0
>>> df_multi_level_cols3.stack(dropna=False)
        height  weight
cat kg     NaN     NaN
    m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
>>> df_multi_level_cols3.stack(dropna=True)
        height  weight
cat m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
method

explode(column, ignore_index=False)

Transform each element of a list-like to a row, replicating index values.

.. versionadded:: 0.25.0

Parameters
  • column (str or tuple or list thereof) Column(s) to explode.For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.
    .. versionadded:: 1.3.0 Multi-column explode
  • ignore_index (bool, default False) If True, the resulting index will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.1.0
Returns (DataFrame)

Exploded lists to rows of the subset columns;index will be duplicated for these rows.

Raises
  • ValueError
    • If columns of the frame are not unique.
    • If specified columns to explode is empty list.
    • If specified columns to explode have not matching count of elements rowwise in the frame.
See Also

DataFrame.unstack : Pivot a level of the (necessarily hierarchical) index labels. DataFrame.melt : Unpivot a DataFrame from wide format to long format. Series.explode : Explode a DataFrame from list-like columns to long format.

Notes

This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.

Examples
>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],...                    'B': 1,
...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode('A')
     A  B          C
0    0  1  [a, b, c]
0    1  1  [a, b, c]
0    2  1  [a, b, c]
1  foo  1        NaN
2  NaN  1         []
3    3  1     [d, e]
3    4  1     [d, e]

Multi-column explode.

>>> df.explode(list('AC'))
     A  B    C
0    0  1    a
0    1  1    b
0    2  1    c
1  foo  1  NaN
2  NaN  1  NaN
3    3  1    d
3    4  1    e
method

unstack(level=-1, fill_value=None)

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Parameters
  • level (int, str, or list of these, default -1 (last level)) Level(s) of index to unstack, can pass level name.
  • fill_value (int, str or dict) Replace NaN with this value if the unstack produces missing values.
See Also

DataFrame.pivot : Pivot a table based on column values.DataFrame.stack : Pivot a level of the column labels (inverse operation from unstack).

Examples
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),...                                    ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64
>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0
>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64
method

melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'.

Parameters
  • id_vars (tuple, list, or ndarray, optional) Column(s) to use as identifier variables.
  • value_vars (tuple, list, or ndarray, optional) Column(s) to unpivot. If not specified, uses all columns thatare not set as id_vars.
  • var_name (scalar) Name to use for the 'variable' column. If None it usesframe.columns.name or 'variable'.
  • value_name (scalar, default 'value') Name to use for the 'value' column.
  • col_level (int or str, optional) If columns are a MultiIndex then use this level to melt.
  • ignore_index (bool, default True) If True, original index is ignored. If False, the original index is retained.Index labels will be repeated as necessary.
    .. versionadded:: 1.1.0
Returns (DataFrame)

Unpivoted DataFrame.

See Also

melt : Identical method.pivot_table : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Return reshaped DataFrame organized by given index / column values. DataFrame.explode : Explode a DataFrame from list-like columns to long format.

Examples
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
3  a        C      2
4  b        C      4
5  c        C      6

The names of 'variable' and 'value' columns can be customized:

>>> df.melt(id_vars=['A'], value_vars=['B'],
...         var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

Original index values can be kept around:

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
0  a        C      2
1  b        C      4
2  c        C      6

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]
>>> df
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5
method

diff(periods=1, axis=0)

First discrete difference of element.

Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row).

Parameters
  • periods (int, default 1) Periods to shift for calculating difference, accepts negativevalues.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Take difference over rows (0) or columns (1).
Returns (Dataframe)

First differences of the Series.

See Also

Dataframe.pct_change: Percent change over given number of periods.Dataframe.shift: Shift index by desired number of periods with an optional time freq. Series.diff: First discrete difference of object.

Notes

For boolean dtypes, this uses :meth:operator.xor rather than :meth:operator.sub. The result is calculated according to current dtype in Dataframe, however dtype of the result is always float64.

Examples

w

, , ) f c 1 4 9 6 5 6

) c N 0 0 0 0 0

n

) c 0 3 7 3 0 8

w

) c N N N 0 0 0

w

) c 0 0 0 0 0 N

e

) ) a N 0

method

aggregate(func=None, axis=0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters
  • func (function, str, list or dict) Function to use for aggregating the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply.
    Accepted combinations are:
    • function
    • string function name
    • list of functions and/or function names, e.g. [np.sum, 'mean']
    • dict of axis labels -> functions, function names or list of such.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.
  • *args Positional arguments to pass to func.
  • **kwargs Keyword arguments to pass to func.
Returns (scalar, Series or DataFrame)

:

n n s

.

e m , d o .

.

See Also

DataFrame.apply : Perform any type of operations.DataFrame.transform : Perform transformation type operations. core.groupby.GroupBy : Perform operations over groups. core.resample.Resampler : Perform operations over resampled bins. core.window.Rolling : Perform operations over rolling window. core.window.Expanding : Perform operations over expanding window. core.window.ExponentialMovingWindow : Perform operation over exponential weighted window.

Notes

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples
>>> df = pd.DataFrame([[1, 2, 3],...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64
method

transform(func, axis=0, *args, **kwargs)

Call func on self producing a DataFrame with transformed values.

Produced DataFrame will have same axis length as self.

Parameters
  • func (function, str, list-like or dict-like) Function to use for transforming the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.
    Accepted combinations are:
    • function
    • string function name
    • list-like of functions and/or function names, e.g. [np.exp, 'sqrt']
    • dict-like of axis labels -> functions, function names or list-like of such.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.
  • *args Positional arguments to pass to func.
  • **kwargs Keyword arguments to pass to func.
Returns (DataFrame)

A DataFrame that must have the same length as self.

See Also

DataFrame.agg : Only perform aggregating type operations.DataFrame.apply : Invoke function on a DataFrame.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

Examples
>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056

You can call transform on a GroupBy object:

>>> df = pd.DataFrame({
...     "Date": [
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df
         Date  Data
0  2015-05-08     5
1  2015-05-07     8
2  2015-05-06     6
3  2015-05-05     1
4  2015-05-08    50
5  2015-05-07   100
6  2015-05-06    60
7  2015-05-05   120
>>> df.groupby('Date')['Data'].transform('sum')
0     55
1    108
2     66
3    121
4     55
5    108
6     66
7    121
Name: Data, dtype: int64
>>> df = pd.DataFrame({
...     "c": [1, 1, 1, 2, 2, 2, 2],
...     "type": ["m", "n", "o", "m", "m", "n", "n"]
... })
>>> df
   c type
0  1    m
1  1    n
2  1    o
3  2    m
4  2    m
5  2    n
6  2    n
>>> df['size'] = df.groupby('c')['type'].transform(len)
>>> df
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4
method

apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame's index (axis=0) or the DataFrame's columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

Parameters
  • func (function) Function to apply to each column or row.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis along which the function is applied:
    • 0 or 'index': apply function to each column.
    • 1 or 'columns': apply function to each row.
  • raw (bool, default False) Determines if row or column is passed as a Series or ndarray object:
    • False : passes each row or column as a Series to the function.
    • True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.
  • result_type ({'expand', 'reduce', 'broadcast', None}, default None) These only act when axis=1 (columns):
    • 'expand' : list-like results will be turned into columns.
    • 'reduce' : returns a Series if possible rather than expanding list-like results. This is the opposite of 'expand'.
    • 'broadcast' : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
    The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.
  • args (tuple) Positional arguments to pass to func in addition to thearray/series.
  • **kwargs Additional keyword arguments to pass as keywords arguments tofunc.
Returns (Series or DataFrame)

Result of applying func along the given axis of theDataFrame.

See Also

DataFrame.applymap: For elementwise operations.DataFrame.aggregate: Only perform aggregating type operations. DataFrame.transform: Only perform transforming type operations.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

Examples
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])>>> df
   A  B
0  4  9
1  4  9
2  4  9

Using a numpy universal function (in this case the same as np.sqrt(df)):

>>> df.apply(np.sqrt)
     A    B
0  2.0  3.0
1  2.0  3.0
2  2.0  3.0

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A    12
B    27
dtype: int64
>>> df.apply(np.sum, axis=1)
0    13
1    13
2    13
dtype: int64

Returning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
   0  1
0  1  2
1  1  2
2  1  2

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2

Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.

>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
   A  B
0  1  2
1  1  2
2  1  2
method

applymap(func, na_action=None, **kwargs)

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters
  • func (callable) Python function, returns a single value from a single value.
  • na_action ({None, 'ignore'}, default None) If ‘ignore’, propagate NaN values, without passing them to func.
    .. versionadded:: 1.2
  • **kwargs Additional keyword arguments to pass as keywords arguments tofunc.
    .. versionadded:: 1.3.0
Returns (DataFrame)

Transformed DataFrame.

See Also

DataFrame.apply : Apply a function along input axis of DataFrame.

Examples
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])>>> df
       0      1
0  1.000  2.120
1  3.356  4.567
>>> df.applymap(lambda x: len(str(x)))
   0  1
0  3  4
1  5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')
      0  1
0  <NA>  4
1     5  5

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.applymap(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it's better to avoid applymap in that case.

>>> df ** 2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489
method

append(other, ignore_index=False, verify_integrity=False, sort=False)

Append rows of other to the end of caller, returning a new object.

Columns in other that are not in the caller are added as new columns.

Parameters
  • other (DataFrame or Series/dict-like object, or list of these) The data to append.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
  • verify_integrity (bool, default False) If True, raise ValueError on creating index with duplicates.
  • sort (bool, default False) Sort columns if the columns of self and other are not aligned.
    .. versionchanged:: 1.0.0
    Changed to not sort by default.
    
Returns (DataFrame)

A new DataFrame consisting of the rows of caller and the rows of other.

See Also

concat : General function to concatenate DataFrame or Series objects.

Notes

If a list of dict/series is passed and the keys are all contained in the DataFrame's index, the order of the columns in the resulting DataFrame will be unchanged.

Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.

Examples
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'), index=['x', 'y'])>>> df
   A  B
x  1  2
y  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'), index=['x', 'y'])
>>> df.append(df2)
   A  B
x  1  2
y  3  4
x  5  6
y  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources.

Less efficient:

>>> df = pd.DataFrame(columns=['A'])
>>> for i in range(5):
...     df = df.append({'A': i}, ignore_index=True)
>>> df
   A
0  0
1  1
2  2
3  3
4  4

More efficient:

>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
...           ignore_index=True)
   A
0  0
1  1
2  2
3  3
4  4
method

join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)

Join columns of another DataFrame.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters
  • other (DataFrame, Series, or list of DataFrame) Index should be similar to one of the columns in this one. If aSeries is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.
  • on (str, list of str, or array-like, optional) Column or index level name(s) in the caller to join on the indexin other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.
  • how ({'left', 'right', 'outer', 'inner'}, default 'left') How to handle the operation of the two objects.
    • left: use calling frame's index (or column if on is specified)
    • right: use other's index.
    • outer: form union of calling frame's index (or column if on is specified) with other's index, and sort it. lexicographically.
    • inner: form intersection of calling frame's index (or column if on is specified) with other's index, preserving the order of the calling's one.
  • lsuffix (str, default '') Suffix to use from left frame's overlapping columns.
  • rsuffix (str, default '') Suffix to use from right frame's overlapping columns.
  • sort (bool, default False) Order result DataFrame lexicographically by the join key. If False,the order of the join key depends on the join type (how keyword).
Returns (DataFrame)

A dataframe containing columns from both the caller and other.

See Also

DataFrame.merge : For column(s)-on-column(s) operations.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Support for specifying index levels as the on parameter was added in version 0.23.0.

Examples
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
  key   A
0  K0  A0
1  K1  A1
2  K2  A2
3  K3  A3
4  K4  A4
5  K5  A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})
>>> other
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix='_caller', rsuffix='_other')
  key_caller   A key_other    B
0         K0  A0        K0   B0
1         K1  A1        K1   B1
2         K2  A2        K2   B2
3         K3  A3       NaN  NaN
4         K4  A4       NaN  NaN
5         K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index('key').join(other.set_index('key'))
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other's index but we can use any column in df. This method preserves the original DataFrame's index in the result.

>>> df.join(other.set_index('key'), on='key')
  key   A    B
0  K0  A0   B0
1  K1  A1   B1
2  K2  A2   B2
3  K3  A3  NaN
4  K4  A4  NaN
5  K5  A5  NaN
method

merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

Merge DataFrame or named Series objects with a database-style join.

A named Series object is treated as a DataFrame with a single named column.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Parameters
  • right (DataFrame or named Series) Object to merge with.
  • how ({'left', 'right', 'outer', 'inner', 'cross'}, default 'inner') Type of merge to be performed.
    • left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
    • right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
    • outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
    • inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
    • cross: creates the cartesian product from both frames, preserves the order of the left keys.
    .. versionadded:: 1.2.0
  • on (label or list) Column or index level names to join on. These must be found in bothDataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.
  • left_on (label or list, or array-like) Column or index level names to join on in the left DataFrame. Can alsobe an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.
  • right_on (label or list, or array-like) Column or index level names to join on in the right DataFrame. Can alsobe an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.
  • left_index (bool, default False) Use the index from the left DataFrame as the join key(s). If it is aMultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.
  • right_index (bool, default False) Use the index from the right DataFrame as the join key. Same caveats asleft_index.
  • sort (bool, default False) Sort the join keys lexicographically in the result DataFrame. If False,the order of the join keys depends on the join type (how keyword).
  • suffixes (list-like, default is ("_x", "_y")) A length-2 sequence where each element is optionally a stringindicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.
  • copy (bool, default True) If False, avoid copy if possible.
  • indicator (bool or str, default False) If True, adds a column to the output DataFrame called "_merge" withinformation on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of "left_only" for observations whose merge key only appears in the left DataFrame, "right_only" for observations whose merge key only appears in the right DataFrame, and "both" if the observation's merge key is found in both DataFrames.
  • validate (str, optional) If specified, checks if merge is of specified type.
    • "one_to_one" or "1:1": check if merge keys are unique in both left and right datasets.
    • "one_to_many" or "1:m": check if merge keys are unique in left dataset.
    • "many_to_one" or "m:1": check if merge keys are unique in right dataset.
    • "many_to_many" or "m:m": allowed, but does not result in checks.
Returns (DataFrame)

A DataFrame of the two merged objects.

See Also

merge_ordered : Merge with optional filling/interpolation.merge_asof : Merge on nearest keys. DataFrame.join : Similar method using indices.

Notes

Support for specifying index levels as the on, left_on, and right_on parameters was added in version 0.23.0 Support for merging named Series objects was added in version 0.24.0

Examples
>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],...                     'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
>>> df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  foo        5  foo        5
3  foo        5  foo        8
4  bar        2  bar        6
5  baz        3  baz        7

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',
...           suffixes=('_left', '_right'))
  lkey  value_left rkey  value_right
0  foo           1  foo            5
1  foo           1  foo            8
2  foo           5  foo            5
3  foo           5  foo            8
4  bar           2  bar            6
5  baz           3  baz            7

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
    Index(['value'], dtype='object')
>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
      a  b
0   foo  1
1   bar  2
>>> df2
      a  c
0   foo  3
1   baz  4
>>> df1.merge(df2, how='inner', on='a')
      a  b  c
0   foo  1  3
>>> df1.merge(df2, how='left', on='a')
      a  b  c
0   foo  1  3.0
1   bar  2  NaN
>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
    left
0   foo
1   bar
>>> df2
    right
0   7
1   8
>>> df1.merge(df2, how='cross')
   left  right
0   foo      7
1   foo      8
2   bar      7
3   bar      8
method

round(decimals=0, *args, **kwargs)

Round a DataFrame to a variable number of decimal places.

Parameters
  • decimals (int, dict, Series) Number of decimal places to round each column to. If an int isgiven, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.
  • *args Additional keywords have no effect but might be accepted forcompatibility with numpy.
  • **kwargs Additional keywords have no effect but might be accepted forcompatibility with numpy.
Returns (DataFrame)

A DataFrame with the affected columns rounded to the specifiednumber of decimal places.

See Also

numpy.around : Round a numpy array to the given number of decimals.Series.round : Round a Series to the given number of decimals.

Examples
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],...                   columns=['dogs', 'cats'])
>>> df
    dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18

By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({'dogs': 1, 'cats': 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0
method

corr(method='pearson', min_periods=1)

Compute pairwise correlation of columns, excluding NA/null values.

Parameters
  • method ({'pearson', 'kendall', 'spearman'} or callable) Method of correlation:
    • pearson : standard correlation coefficient
    • kendall : Kendall Tau correlation coefficient
    • spearman : Spearman rank correlation
    • callable: callable with input two 1d ndarrays and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable's behavior.
  • min_periods (int, optional) Minimum number of observations required per pair of columnsto have a valid result.
Returns (DataFrame)

Correlation matrix.

See Also

DataFrame.corrwith : Compute pairwise correlation with another DataFrame or Series. Series.corr : Compute the correlation between two Series.

Examples
>>> def histogram_intersection(a, b):...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0
method

cov(min_periods=None, ddof=1)

Compute pairwise covariance of columns, excluding NA/null values.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix <https://en.wikipedia.org/wiki/Covariance_matrix>__ of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters
  • min_periods (int, optional) Minimum number of observations required per pair of columnsto have a valid result.
  • ddof (int, default 1) Delta degrees of freedom. The divisor used in calculationsis N - ddof, where N represents the number of elements.
    .. versionadded:: 1.1.0
Returns (DataFrame)

The covariance matrix of the series of the DataFrame.

See Also

Series.cov : Compute covariance with another Series.core.window.ExponentialMovingWindow.cov: Exponential weighted sample covariance. core.window.Expanding.cov : Expanding sample covariance. core.window.Rolling.cov : Rolling sample covariance.

Notes

Returns the covariance matrix of the DataFrame's time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random <https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>__) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices <https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_ matrices>__ for more details.

Examples
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],...                   columns=['dogs', 'cats'])
>>> df.cov()
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202
method

corrwith(other, axis=0, drop=False, method='pearson')

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Parameters
  • other (DataFrame, Series) Object with which to compute correlations.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' to compute column-wise, 1 or 'columns' forrow-wise.
  • drop (bool, default False) Drop missing indices from result.
  • method ({'pearson', 'kendall', 'spearman'} or callable) Method of correlation:
    • pearson : standard correlation coefficient
    • kendall : Kendall Tau correlation coefficient
    • spearman : Spearman rank correlation
    • callable: callable with input two 1d ndarrays and returning a float.
Returns (Series)

Pairwise correlations.

See Also

DataFrame.corr : Compute pairwise correlation of columns.

method

count(axis=0, level=None, numeric_only=False)

Count non-NA cells for each column or row.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index' counts are generated for each column.If 1 or 'columns' counts are generated for each row.
  • level (int or str, optional) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a DataFrame. A str specifies the level name.
  • numeric_only (bool, default False) Include only float, int or boolean data.
Returns (Series or DataFrame)

For each column/row the number of non-NA/null entries.If level is specified returns a DataFrame.

See Also

Series.count: Number of non-NA elements in a Series.DataFrame.value_counts: Count unique combinations of columns. DataFrame.shape: Number of DataFrame rows and columns (including NA elements). DataFrame.isna: Boolean same-sized DataFrame showing places of NA elements.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis='columns')
0    3
1    2
2    3
3    3
4    3
dtype: int64
method

nunique(axis=0, dropna=True)

Count number of distinct elements in specified axis.

Return Series with number of distinct elements. Can ignore NaN values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' forcolumn-wise.
  • dropna (bool, default True) Don't include NaN in the counts.
See Also

Series.nunique: Method nunique for Series.DataFrame.count: Count non-NA cells for each column or row.

Examples
>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})>>> df.nunique()
A    3
B    2
dtype: int64
>>> df.nunique(axis=1)
0    1
1    2
2    2
dtype: int64
method

idxmin(axis=0, skipna=True)

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series)

Indexes of minima along the specified axis.

Raises
  • ValueError
    • If the row/column is empty
See Also

Series.idxmin : Return index of the minimum element.

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object
method

idxmax(axis=0, skipna=True)

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series)

Indexes of maxima along the specified axis.

Raises
  • ValueError
    • If the row/column is empty
See Also

Series.idxmax : Return index of the maximum element.

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object
method

mode(axis=0, numeric_only=False, dropna=True)

Get the mode(s) of each element along the selected axis.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to iterate over while searching for the mode:
    • 0 or 'index' : get mode of each column
    • 1 or 'columns' : get mode of each row.
  • numeric_only (bool, default False) If True, only apply to numeric columns.
  • dropna (bool, default True) Don't consider counts of NaN/NaT.
Returns (DataFrame)

The modes of each column or row.

See Also

Series.mode : Return the highest frequency value in a Series.Series.value_counts : Return the counts of values in a Series.

Examples
>>> df = pd.DataFrame([('bird', 2, 2),...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN.

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis='columns', numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN
method

quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Return values at the given quantile over requested axis.

Parameters
  • q (float or array-like, default 0.5 (50% quantile)) Value between 0 <= q <= 1, the quantile(s) to compute.
  • axis ({0, 1, 'index', 'columns'}, default 0) Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • numeric_only (bool, default True) If False, the quantile of datetime and timedelta data will becomputed as well.
  • interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) This optional parameter specifies the interpolation method to use,when the desired quantile lies between two data points i and j:
    • linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
    • lower: i.
    • higher: j.
    • nearest: i or j whichever is nearest.
    • midpoint: (i + j) / 2.
Returns (Series or DataFrame)

ee . e .

See Also

core.window.Rolling.quantile: Rolling quantile.numpy.percentile: Numpy function to compute the percentile.

Examples
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),...                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

>>> df = pd.DataFrame({'A': [1, 2],
...                    'B': [pd.Timestamp('2010'),
...                          pd.Timestamp('2011')],
...                    'C': [pd.Timedelta('1 days'),
...                          pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object
method

asfreq(freq, method=None, how=None, normalize=False, fill_value=None)

Convert time series to specified frequency.

Returns the original data conformed to a new index with the specified frequency.

If the index of this DataFrame is a :class:~pandas.PeriodIndex, the new index is the result of transforming the original index with :meth:PeriodIndex.asfreq <pandas.PeriodIndex.asfreq> (so the original index will map one-to-one to the new index).

Otherwise, the new index will be equivalent to pd.date_range(start, end, freq=freq) where start and end are, respectively, the first and last entries in the original index (see :func:pandas.date_range). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see the method parameter below).

The :meth:resample method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.

Parameters
  • freq (DateOffset or str) Frequency DateOffset or string.
  • method ({'backfill'/'bfill', 'pad'/'ffill'}, default None) Method to use for filling holes in reindexed Series (note thisdoes not fill NaNs that already were present):
    • 'pad' / 'ffill': propagate last valid observation forward to next valid
    • 'backfill' / 'bfill': use NEXT valid observation to fill.
  • how ({'start', 'end'}, default end) For PeriodIndex only (see PeriodIndex.asfreq).
  • normalize (bool, default False) Whether to reset output index to midnight.
  • fill_value (scalar, optional) Value to use for missing values, applied during upsampling (notethis does not fill NaNs that already were present).
Returns (DataFrame)

DataFrame object reindexed to the specified frequency.

See Also

reindex : Conform DataFrame to new index with optional filling logic.

Notes

To learn more about the frequency strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s': series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30S', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30S', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0
method

resample(rule, axis=0, closed=None, label=None, convention='start', kind=None, loffset=None, base=None, on=None, level=None, origin='start_day', offset=None)

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the on/level keyword parameter.

Parameters
  • rule (DateOffset, Timedelta or str) The offset string or object representing target conversion.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Which axis to use for up- or down-sampling. For Series thiswill default to 0, i.e. along the rows. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.
  • closed ({'right', 'left'}, default None) Which side of bin interval is closed. The default is 'left'for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'.
  • label ({'right', 'left'}, default None) Which bin edge label to label bucket with. The default is 'left'for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'.
  • convention ({'start', 'end', 's', 'e'}, default 'start') For PeriodIndex only, controls whether to use the start orend of rule.
  • kind ({'timestamp', 'period'}, optional, default None) Pass 'timestamp' to convert the resulting index to aDateTimeIndex or 'period' to convert it to a PeriodIndex. By default the input representation is retained.
  • loffset (timedelta, default None) Adjust the resampled time labels.
    .. deprecated:: 1.1.0 You should add the loffset to the df.index after the resample. See below.
  • base (int, default 0) For frequencies that evenly subdivide 1 day, the "origin" of theaggregated intervals. For example, for '5min' frequency, base could range from 0 through 4. Defaults to 0.
    .. deprecated:: 1.1.0 The new arguments that you should use are 'offset' or 'origin'.
  • on (str, optional) For a DataFrame, column to use instead of index for resampling.Column must be datetime-like.
  • level (str or int, optional) For a MultiIndex, level (name or number) to use forresampling. level must be datetime-like.
  • origin ({'epoch', 'start', 'start_day', 'end', 'end_day'}, Timestamp) or str, default 'start_day'The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a timestamp is not used, these values are also supported:
    • 'epoch': origin is 1970-01-01
    • 'start': origin is the first value of the timeseries
    • 'start_day': origin is the first day at midnight of the timeseries
    .. versionadded:: 1.1.0
    • 'end': origin is the last value of the timeseries
    • 'end_day': origin is the ceiling midnight of the last day
    .. versionadded:: 1.3.0
  • offset (Timedelta or str, default is None) An offset timedelta added to the origin.
    .. versionadded:: 1.1.0
Returns (pandas.core.Resampler)

:class:~pandas.core.Resampler object.

See Also

Series.resample : Resample a Series.DataFrame.resample : Resample a DataFrame. groupby : Group DataFrame by mapping, function, label, or list of labels. asfreq : Reindex a DataFrame with the given frequency without grouping.

Notes

See the user guide <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>__ for more.

To learn more about the offset strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>__.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: T, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample('3T').sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one.

>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but close the right side of the bin interval.

>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3T, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample('30S').asfreq()[0:5]   # Select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30S, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the pad method.

>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(arraylike):
...     return np.sum(arraylike) + 5
...
>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3T, dtype: int64

For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.

Resample a year by quarter using 'start' convention. Values are assigned to the first quarter of the period.

>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
...                                             freq='A',
...                                             periods=2))
>>> s
2012    1
2013    2
Freq: A-DEC, dtype: int64
>>> s.resample('Q', convention='start').asfreq()
2012Q1    1.0
2012Q2    NaN
2012Q3    NaN
2012Q4    NaN
2013Q1    2.0
2013Q2    NaN
2013Q3    NaN
2013Q4    NaN
Freq: Q-DEC, dtype: float64

Resample quarters by month using 'end' convention. Values are assigned to the last month of the period.

>>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',
...                                                   freq='Q',
...                                                   periods=4))
>>> q
2018Q1    1
2018Q2    2
2018Q3    3
2018Q4    4
Freq: Q-DEC, dtype: int64
>>> q.resample('M', convention='end').asfreq()
2018-03    1.0
2018-04    NaN
2018-05    NaN
2018-06    2.0
2018-07    NaN
2018-08    NaN
2018-09    3.0
2018-10    NaN
2018-11    NaN
2018-12    4.0
Freq: M, dtype: float64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
...                                     periods=8,
...                                     freq='W')
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
>>> df.resample('M', on='week_starting').mean()
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df2 = pd.DataFrame(
...     d2,
...     index=pd.MultiIndex.from_product(
...         [days, ['morning', 'afternoon']]
...     )
... )
>>> df2
                      price  volume
2000-01-01 morning       10      50
           afternoon     11      60
2000-01-02 morning        9      40
           afternoon     13     100
2000-01-03 morning       14      50
           afternoon     18     100
2000-01-04 morning       17      40
           afternoon     19      50
>>> df2.resample('D', level=0).sum()
            price  volume
2000-01-01     21     110
2000-01-02     22     140
2000-01-03     32     150
2000-01-04     36      90

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7T, dtype: int64
>>> ts.resample('17min').sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17T, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64

If you want to take the largest Timestamp as the end of the bins:

>>> ts.resample('17min', origin='end').sum()
2000-10-01 23:35:00     0
2000-10-01 23:52:00    18
2000-10-02 00:09:00    27
2000-10-02 00:26:00    63
Freq: 17T, dtype: int64

In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

>>> ts.resample('17min', origin='end_day').sum()
2000-10-01 23:38:00     3
2000-10-01 23:55:00    15
2000-10-02 00:12:00    45
2000-10-02 00:29:00    45
Freq: 17T, dtype: int64

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2:

>>> ts.resample('17min', offset='2min').sum()
2000-10-01 23:16:00     0
2000-10-01 23:33:00     9
2000-10-01 23:50:00    36
2000-10-02 00:07:00    39
2000-10-02 00:24:00    24
Freq: 17T, dtype: int64

To replace the use of the deprecated loffset argument:

>>> from pandas.tseries.frequencies import to_offset
>>> loffset = '19min'
>>> ts_out = ts.resample('17min').sum()
>>> ts_out.index = ts_out.index + to_offset(loffset)
>>> ts_out
2000-10-01 23:33:00     0
2000-10-01 23:50:00     9
2000-10-02 00:07:00    21
2000-10-02 00:24:00    54
2000-10-02 00:41:00    24
Freq: 17T, dtype: int64
method

to_timestamp(freq=None, how='start', axis=0, copy=True)

Cast to DatetimeIndex of timestamps, at beginning of period.

Parameters
  • freq (str, default frequency of PeriodIndex) Desired frequency.
  • how ({'s', 'e', 'start', 'end'}) Convention for converting period to timestamp; start of periodvs. end.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to convert (the index by default).
  • copy (bool, default True) If False then underlying input data is not copied.
method

to_period(freq=None, axis=0, copy=True)

Convert DataFrame from DatetimeIndex to PeriodIndex.

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).

Parameters
  • freq (str, default) Frequency of the PeriodIndex.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to convert (the index by default).
  • copy (bool, default True) If False then underlying input data is not copied.
method

isin(values)

Whether each element in the DataFrame is contained in values.

Parameters
  • values (iterable, Series, DataFrame or dict) The result will only be true at a location if all thelabels match. If values is a Series, that's the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.
Returns (DataFrame)

DataFrame of booleans showing whether each element in the DataFrameis contained in values.

See Also

DataFrame.eq: Equality test for DataFrame.Series.isin: Equivalent method on Series. Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},...                   index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that 'falcon' does not match based on the number of legs in df2.

>>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
...                      index=['spider', 'falcon'])
>>> df.isin(other)
        num_legs  num_wings
falcon      True       True
dog        False      False
method

copy(deep=True, copy_grouped=False)DataFrameGroupByABC

Copy the dataframe and keep the class

Parameters
  • deep (bool, optional) Make a deep copy, including a copy of the data and the indices.With deep=False neither the indices nor the data are copied.
class

datar.core.grouped.DataFrameRowwise(data, _group_vars=None, _group_drop=None, _group_data=None, **kwargs)

Bases
datar.core.grouped.DataFrameGroupBy datar.core.grouped.DataFrameGroupByABC pandas.core.frame.DataFrame pandas.core.generic.NDFrame pandas.core.base.PandasObject pandas.core.accessor.DirNamesMixin pandas.core.indexing.IndexingMixin pandas.core.arraylike.OpsMixin

Rowwise dataframe

Parameters
  • data (any) Data that used to construct the frame.
  • _group_vars (Union(str, str_, iterable of str or str_), optional) The grouping variables
  • _group_drop (bool, optional) Whether to drop non-observable rows
  • _group_data (DataFrame, optional) In most cases, used for copy. Use this groupdataif provided to avoid recalculate.
  • **kwargs (any) Additional keyword arguments passed to DataFrame constructor.
Attributes
  • _grouped_df The grouped data frame (pandas' DataFrameGroupBy object)
  • at (_AtIndexer) Access a single value for a row/column label pair.
    Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series. </>
  • attrs (dict) Dictionary of global attributes of this dataset.
    .. warning::
    attrs is experimental and may change without warning. </>
  • axes (list) Return a list representing the axes of the DataFrame.
    It has the row axis labels and column axis labels as the only members. They are returned in that order. </>
  • dtypes Return the dtypes in the DataFrame.
    This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the object dtype. See :ref:the User Guide <basics.dtypes> for more. </>
  • empty Indicator whether DataFrame is empty.
    True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0. </>
  • flags (Flags) Get the properties associated with this pandas object.
    The available flags are
    • :attr:Flags.allows_duplicate_labels
    </>
  • iat (_iAtIndexer) Access a single value for a row/column pair by integer position.
    Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series. </>
  • iloc (_iLocIndexer) Purely integer-location based indexing for selection by position.
    .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.
    Allowed inputs are:
    • An integer, e.g. 5.
    • A list or array of integers, e.g. [4, 3, 0].
    • A slice object with ints, e.g. 1:7.
    • A boolean array.
    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don't have a reference to the calling object, but would like to base your selection on some value.
    .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).
    See more at :ref:Selection by Position <indexing.integer>. </>
  • loc (_LocIndexer) Access a group of rows and columns by label(s) or a boolean array.
    .loc[] is primarily label based, but may also be used with a boolean array.
    Allowed inputs are:
    • A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
    • A list or array of labels, e.g. ['a', 'b', 'c'].
    • A slice object with labels, e.g. 'a':'f'.
    .. warning:: Note that contrary to usual python slices, both the start and the stop are included
    • A boolean array of the same length as the axis being sliced, e.g. [True, False, True].
    • An alignable boolean Series. The index of the key will be aligned before masking.
    • An alignable Index. The Index of the returned selection will be the input.
    • A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above)
    See more at :ref:Selection by Label <indexing.label>. </>
  • ndim (int) Return an int representing the number of axes / array dimensions.
    Return 1 if Series. Otherwise return 2 if DataFrame. </>
  • shape (tuple) Return a tuple representing the dimensionality of the DataFrame.</>
  • size (int) Return an int representing the number of elements in this object.
    Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. </>
  • style (Styler) Returns a Styler object.
    Contains methods for building a styled HTML representation of the DataFrame. </>
  • values Return a Numpy representation of the DataFrame.
    .. warning::
    We recommend using :meth:DataFrame.to_numpy instead.
    Only the values in the DataFrame will be returned, the axes labels will be removed. </>
Methods
  • __array_wrap__(result, context) Gets called after a ufunc and other functions.</>
  • __contains__(key) (bool) True if the key is in the info axis</>
  • __delitem__(key) Delete item</>
  • __dir__() (list) Provide method name lookup and completion.</>
  • __finalize__(other, method, **kwargs) (FrameOrSeries) Propagate metadata from other to self.</>
  • __getattr__(name) After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use. </>
  • __iter__() (iterator) Iterate over info axis.</>
  • __len__() (int) Returns length of info axis, but here we use the index.</>
  • __matmul__(other) (DataFrame or Series) Matrix multiplication using binary @ operator in Python>=3.5.</>
  • __repr__() (str) Return a string representation for a particular DataFrame.</>
  • __rmatmul__(other) Matrix multiplication using binary @ operator in Python>=3.5.</>
  • __setattr__(name, value) After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use. </>
  • __sizeof__() (int) Generates the total memory usage for an object that returnseither a value or Series of values </>
  • abs() (abs) Return a Series/DataFrame with absolute numeric value of each element.</>
  • add_prefix(prefix) (Series or DataFrame) Prefix labels with string prefix.</>
  • add_suffix(suffix) (Series or DataFrame) Suffix labels with string suffix.</>
  • aggregate(func, axis, *args, **kwargs) (scalar, Series or DataFrame) Aggregate using one or more operations over the specified axis.</>
  • align(other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis) (left, right) : (DataFrame, type of other) Align two objects on their axes with the specified join method.</>
  • all(axis, bool_only, skipna, level, **kwargs) (Series or DataFrame) Return whether all elements are True, potentially over an axis.</>
  • any(axis, bool_only, skipna, level, **kwargs) (Series or DataFrame) Return whether any element is True, potentially over an axis.</>
  • append(other, ignore_index, verify_integrity, sort) (DataFrame) Append rows of other to the end of caller, returning a new object.</>
  • apply(func, axis, raw, result_type, args, **kwargs) (Series or DataFrame) Apply a function along an axis of the DataFrame.</>
  • applymap(func, na_action, **kwargs) (DataFrame) Apply a function to a Dataframe elementwise.</>
  • asfreq(freq, method, how, normalize, fill_value) (DataFrame) Convert time series to specified frequency.</>
  • asof(where, subset) (scalar, Series, or DataFrame) Return the last row(s) without any NaNs before where.</>
  • assign(**kwargs) (DataFrame) Assign new columns to a DataFrame.</>
  • astype(dtype, copy, errors) (casted : same type as caller) Cast a pandas object to a specified dtype dtype.</>
  • at_time(time, asof, axis) (Series or DataFrame) Select values at particular time of day (e.g., 9:30AM).</>
  • between_time(start_time, end_time, include_start, include_end, axis) (Series or DataFrame) Select values between particular times of the day (e.g., 9:00-9:30 AM).</>
  • bfill(axis, inplace, limit, downcast) (Series/DataFrame or None) Synonym for :meth:DataFrame.fillna with method='bfill'.</>
  • bool() (bool) Return the bool of a single element Series or DataFrame.</>
  • combine(other, func, fill_value, overwrite) (DataFrame) Perform column-wise combine with another DataFrame.</>
  • combine_first(other) (DataFrame) Update null elements with value in the same location in other.</>
  • compare(other, align_axis, keep_shape, keep_equal) (DataFrame) Compare to another DataFrame and show the differences.</>
  • convert_dtypes(infer_objects, convert_string, convert_integer, convert_boolean, convert_floating) (Series or DataFrame) Convert columns to best possible dtypes using dtypes supporting pd.NA.</>
  • copy(deep, copy_grouped) (DataFrameGroupByABC) Copy the dataframe and keep the class</>
  • corr(method, min_periods) (DataFrame) Compute pairwise correlation of columns, excluding NA/null values.</>
  • corrwith(other, axis, drop, method) (Series) Compute pairwise correlation.</>
  • count(axis, level, numeric_only) (Series or DataFrame) Count non-NA cells for each column or row.</>
  • cov(min_periods, ddof) (DataFrame) Compute pairwise covariance of columns, excluding NA/null values.</>
  • cummax(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative maximum over a DataFrame or Series axis.</>
  • cummin(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative minimum over a DataFrame or Series axis.</>
  • cumprod(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative product over a DataFrame or Series axis.</>
  • cumsum(axis, skipna, *args, **kwargs) (Series or DataFrame) Return cumulative sum over a DataFrame or Series axis.</>
  • describe(percentiles, include, exclude, datetime_is_numeric) (Series or DataFrame) Generate descriptive statistics.</>
  • diff(periods, axis) (Dataframe) First discrete difference of element.</>
  • dot(other) (Series or DataFrame) Compute the matrix multiplication between the DataFrame and other.</>
  • drop(labels, axis, index, columns, level, inplace, errors) (DataFrame or None) Drop specified labels from rows or columns.</>
  • drop_duplicates(subset, keep, inplace, ignore_index) (DataFrame or None) Return DataFrame with duplicate rows removed.</>
  • droplevel(level, axis) (Series/DataFrame) Return Series/DataFrame with requested index / column level(s) removed.</>
  • dropna(axis, how, thresh, subset, inplace) (DataFrame or None) Remove missing values.</>
  • duplicated(subset, keep) (Series) Return boolean Series denoting duplicate rows.</>
  • equals(other) (bool) Test whether two objects contain the same elements.</>
  • eval(expr, inplace, **kwargs) (ndarray, scalar, pandas object, or None) Evaluate a string describing operations on DataFrame columns.</>
  • ewm(com, span, halflife, alpha, min_periods, adjust, ignore_na, axis, times) (DataFrame) Provide exponential weighted (EW) functions.</>
  • expanding(min_periods, center, axis, method) (a Window sub-classed for the particular operation) Provide expanding transformations.</>
  • explode(column, ignore_index) (DataFrame) Transform each element of a list-like to a row, replicating index values.</>
  • ffill(axis, inplace, limit, downcast) (Series/DataFrame or None) Synonym for :meth:DataFrame.fillna with method='ffill'.</>
  • fillna(value, method, axis, inplace, limit, downcast) (DataFrame or None) Fill NA/NaN values using the specified method.</>
  • filter(items, like, regex, axis) (same type as input object) Subset the dataframe rows or columns according to the specified index labels.</>
  • first(offset) (Series or DataFrame) Select initial periods of time series data based on a date offset.</>
  • first_valid_index() (scalar : type of index) Return index for first non-NA value or None, if no NA value is found.</>
  • from_dict(data, orient, dtype, columns) (DataFrame) Construct DataFrame from dict of array-like or dicts.</>
  • from_records(data, index, exclude, columns, coerce_float, nrows) (DataFrame) Convert structured or record ndarray to DataFrame.</>
  • get(key, default) (value : same type as items contained in object) Get item from object for given key (ex: DataFrame column).</>
  • groupby(by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna) (DataFrameGroupBy) Group DataFrame using a mapper or by a Series of columns.</>
  • head(n) (same type as caller) Return the first n rows.</>
  • idxmax(axis, skipna) (Series) Return index of first occurrence of maximum over requested axis.</>
  • idxmin(axis, skipna) (Series) Return index of first occurrence of minimum over requested axis.</>
  • infer_objects() (converted : same type as input object) Attempt to infer better dtypes for object columns.</>
  • info(verbose, buf, max_cols, memory_usage, show_counts, null_counts) (None) Print a concise summary of a DataFrame.</>
  • insert(loc, column, value, allow_duplicates) Insert column into DataFrame at specified location.</>
  • isin(values) (DataFrame) Whether each element in the DataFrame is contained in values.</>
  • isna() (DataFrame) Detect missing values.</>
  • isnull() (DataFrame) Detect missing values.</>
  • items() (label : object) Iterate over (column name, Series) pairs.</>
  • iteritems() (label : object) Iterate over (column name, Series) pairs.</>
  • iterrows() (index : label or tuple of label) Iterate over DataFrame rows as (index, Series) pairs.</>
  • itertuples(index, name) (iterator) Iterate over DataFrame rows as namedtuples.</>
  • join(other, on, how, lsuffix, rsuffix, sort) (DataFrame) Join columns of another DataFrame.</>
  • keys() (Index) Get the 'info axis' (see Indexing for more).</>
  • kurt(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased kurtosis over requested axis.</>
  • last(offset) (Series or DataFrame) Select final periods of time series data based on a date offset.</>
  • last_valid_index() (scalar : type of index) Return index for last non-NA value or None, if no NA value is found.</>
  • lookup(row_labels, col_labels) (numpy.ndarray) Label-based "fancy indexing" function for DataFrame.Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. </>
  • mad(axis, skipna, level) (Series or DataFrame (if level specified)) Return the mean absolute deviation of the values over the requested axis.</>
  • max(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the maximum of the values over the requested axis.</>
  • mean(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the mean of the values over the requested axis.</>
  • median(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the median of the values over the requested axis.</>
  • melt(id_vars, value_vars, var_name, value_name, col_level, ignore_index) (DataFrame) Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.</>
  • memory_usage(index, deep) (Series) Return the memory usage of each column in bytes.</>
  • merge(right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate) (DataFrame) Merge DataFrame or named Series objects with a database-style join.</>
  • min(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return the minimum of the values over the requested axis.</>
  • mode(axis, numeric_only, dropna) (DataFrame) Get the mode(s) of each element along the selected axis.</>
  • nlargest(n, columns, keep) (DataFrame) Return the first n rows ordered by columns in descending order.</>
  • notna() (DataFrame) Detect existing (non-missing) values.</>
  • notnull() (DataFrame) Detect existing (non-missing) values.</>
  • nsmallest(n, columns, keep) (DataFrame) Return the first n rows ordered by columns in ascending order.</>
  • nunique(axis, dropna) (Series) Count number of distinct elements in specified axis.</>
  • pct_change(periods, fill_method, limit, freq, **kwargs) (chg : Series or DataFrame) Percentage change between the current and a prior element.</>
  • pipe(func, *args, **kwargs) (object : the return type of ``func``.) Apply func(self, *args, **kwargs).</>
  • pivot(index, columns, values) (DataFrame) Return reshaped DataFrame organized by given index / column values.</>
  • pivot_table(values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort) (DataFrame) Create a spreadsheet-style pivot table as a DataFrame.</>
  • pop(item) (Series) Return item and drop from frame. Raise KeyError if not found.</>
  • prod(axis, skipna, level, numeric_only, min_count, **kwargs) (Series or DataFrame (if level specified)) Return the product of the values over the requested axis.</>
  • quantile(q, axis, numeric_only, interpolation) (Series or DataFrame) Return values at the given quantile over requested axis.</>
  • query(expr, inplace, **kwargs) (DataFrame or None) Query the columns of a DataFrame with a boolean expression.</>
  • rank(axis, method, numeric_only, na_option, ascending, pct) (same type as caller) Compute numerical data ranks (1 through n) along axis.</>
  • reindex_like(other, method, copy, limit, tolerance) (Series or DataFrame) Return an object with matching indices as other object.</>
  • rename(mapper, index, columns, axis, copy, inplace, level, errors) (DataFrame or None) Alter axes labels.</>
  • rename_axis(mapper, index, columns, axis, copy, inplace) (Series, DataFrame, or None) Set the name of the axis for the index or columns.</>
  • reorder_levels(order, axis) (DataFrame) Rearrange index levels using input order. May not drop or duplicate levels.</>
  • replace(to_replace, value, inplace, limit, regex, method) (DataFrame) Replace values given in to_replace with value.</>
  • resample(rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset) (pandas.core.Resampler) Resample time-series data.</>
  • reset_index(level, drop, inplace, col_level, col_fill) (DataFrame or None) Reset the index, or a level of it.</>
  • rolling(window, min_periods, center, win_type, on, axis, closed, method) (a Window or Rolling sub-classed for the particular operation) Provide rolling window calculations.</>
  • round(decimals, *args, **kwargs) (DataFrame) Round a DataFrame to a variable number of decimal places.</>
  • sample(n, frac, replace, weights, random_state, axis, ignore_index) (Series or DataFrame) Return a random sample of items from an axis of object.</>
  • select_dtypes(include, exclude) (DataFrame) Return a subset of the DataFrame's columns based on the column dtypes.</>
  • sem(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased standard error of the mean over requested axis.</>
  • set_axis(labels, axis, inplace) (renamed : DataFrame or None) Assign desired index to given axis.</>
  • set_flags(copy, allows_duplicate_labels) (Series or DataFrame) Return a new object with updated flags.</>
  • set_index(keys, drop, append, inplace, verify_integrity) (DataFrame or None) Set the DataFrame index using existing columns.</>
  • shift(periods, freq, axis, fill_value) (DataFrame) Shift index by desired number of periods with an optional time freq.</>
  • skew(axis, skipna, level, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased skew over requested axis.</>
  • slice_shift(periods, axis) (shifted : same type as caller) Equivalent to shift without copying data.The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. </>
  • sort_index(axis, level, ascending, inplace, kind, na_position, sort_remaining, ignore_index, key) (DataFrame or None) Sort object by labels (along an axis).</>
  • sort_values(by, axis, ascending, inplace, kind, na_position, ignore_index, key) (DataFrame or None) Sort by the values along either axis.</>
  • squeeze(axis) (DataFrame, Series, or scalar) Squeeze 1 dimensional axis objects into scalars.</>
  • stack(level, dropna) (DataFrame or Series) Stack the prescribed level(s) from columns to index.</>
  • std(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return sample standard deviation over requested axis.</>
  • sum(axis, skipna, level, numeric_only, min_count, **kwargs) (Series or DataFrame (if level specified)) Return the sum of the values over the requested axis.</>
  • swapaxes(axis1, axis2, copy) (y : same as input) Interchange axes and swap values axes appropriately.</>
  • swaplevel(i, j, axis) (DataFrame) Swap levels i and j in a :class:MultiIndex.</>
  • tail(n) (type of caller) Return the last n rows.</>
  • take(indices, axis, is_copy, **kwargs) (taken : same type as caller) Return the elements in the given positional indices along an axis.</>
  • to_clipboard(excel, sep, **kwargs) Copy object to the system clipboard.</>
  • to_csv(path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options) (None or str) Write object to a comma-separated values (csv) file.</>
  • to_dict(orient, into) (dict, list or collections.abc.Mapping) Convert the DataFrame to a dictionary.</>
  • to_excel(excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, encoding, inf_rep, verbose, freeze_panes, storage_options) Write object to an Excel sheet.</>
  • to_feather(path, **kwargs) Write a DataFrame to the binary Feather format.</>
  • to_gbq(destination_table, project_id, chunksize, reauth, if_exists, auth_local_webserver, table_schema, location, progress_bar, credentials) Write a DataFrame to a Google BigQuery table.</>
  • to_hdf(path_or_buf, key, mode, complevel, complib, append, format, index, min_itemsize, nan_rep, dropna, data_columns, errors, encoding) Write the contained data to an HDF5 file using HDFStore.</>
  • to_html(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, max_cols, show_dimensions, decimal, bold_rows, classes, escape, notebook, border, table_id, render_links, encoding) (str or None) Render a DataFrame as an HTML table.</>
  • to_json(path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index, indent, storage_options) (None or str) Convert the object to a JSON string.</>
  • to_latex(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, bold_rows, column_format, longtable, escape, encoding, decimal, multicolumn, multicolumn_format, multirow, caption, label, position) Render object to a LaTeX tabular, longtable, or nested table/tabular.</>
  • to_markdown(buf, mode, index, storage_options, **kwargs) (str) Print DataFrame in Markdown-friendly format.</>
  • to_numpy(dtype, copy, na_value) (numpy.ndarray) Convert the DataFrame to a NumPy array.</>
  • to_parquet(path, engine, compression, index, partition_cols, storage_options, **kwargs) (bytes if no path argument is provided else None) Write a DataFrame to the binary parquet format.</>
  • to_period(freq, axis, copy) (DataFrame with PeriodIndex) Convert DataFrame from DatetimeIndex to PeriodIndex.</>
  • to_pickle(path, compression, protocol, storage_options) Pickle (serialize) object to file.</>
  • to_records(index, column_dtypes, index_dtypes) (numpy.recarray) Convert DataFrame to a NumPy record array.</>
  • to_sql(name, con, schema, if_exists, index, index_label, chunksize, dtype, method) Write records stored in a DataFrame to a SQL database.</>
  • to_stata(path, convert_dates, write_index, byteorder, time_stamp, data_label, variable_labels, version, convert_strl, compression, storage_options) Export DataFrame object to Stata dta format.</>
  • to_string(buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, min_rows, max_cols, show_dimensions, decimal, line_width, max_colwidth, encoding) (str or None) Render a DataFrame to a console-friendly tabular output.</>
  • to_timestamp(freq, how, axis, copy) (DataFrame with DatetimeIndex) Cast to DatetimeIndex of timestamps, at beginning of period.</>
  • to_xarray() (xarray.DataArray or xarray.Dataset) Return an xarray object from the pandas object.</>
  • to_xml(path_or_buffer, index, root_name, row_name, na_rep, attr_cols, elem_cols, namespaces, prefix, encoding, xml_declaration, pretty_print, parser, stylesheet, compression, storage_options) (None or str) Render a DataFrame to an XML document.</>
  • transform(func, axis, *args, **kwargs) (DataFrame) Call func on self producing a DataFrame with transformed values.</>
  • transpose(*args, copy) (DataFrame) Transpose index and columns.</>
  • truncate(before, after, axis, copy) (type of caller) Truncate a Series or DataFrame before and after some index value.</>
  • tshift(periods, freq, axis) (shifted : Series/DataFrame) Shift the time index, using the index's frequency if available.</>
  • tz_convert(tz, axis, level, copy) ({klass}) Convert tz-aware axis to target time zone.</>
  • tz_localize(tz, axis, level, copy, ambiguous, nonexistent) (Series or DataFrame) Localize tz-naive index of a Series or DataFrame to target time zone.</>
  • unstack(level, fill_value) (Series or DataFrame) Pivot a level of the (necessarily hierarchical) index labels.</>
  • update(other, join, overwrite, filter_func, errors) (None : method directly changes calling object) Modify in place using non-NA values from another DataFrame.</>
  • value_counts(subset, normalize, sort, ascending, dropna) (Series) Return a Series containing counts of unique rows in the DataFrame.</>
  • var(axis, skipna, level, ddof, numeric_only, **kwargs) (Series or DataFrame (if level specified)) Return unbiased variance over requested axis.</>
  • xs(key, axis, level, drop_level) (Series or DataFrame) Return cross-section from the Series/DataFrame.</>
method

__dir__() → list

Provide method name lookup and completion.

Notes

Only provide 'public' methods.

method

__sizeof__() → int

Generates the total memory usage for an object that returnseither a value or Series of values

method

set_flags(copy=False, allows_duplicate_labels=None)

Return a new object with updated flags.

Parameters
  • allows_duplicate_labels (bool, optional) Whether the returned object allows duplicate labels.
Returns (Series or DataFrame)

The same type as the caller.

See Also

DataFrame.attrs : Global metadata applying to this dataset.DataFrame.flags : Global flags applying to this object.

Notes

This method returns a new object that's a view on the same data as the input. Mutating the input or the output values will be reflected in the other.

This method is intended to be used in method chains.

"Flags" differ from "metadata". Flags reflect properties of the pandas object (the Series or DataFrame). Metadata refer to properties of the dataset, and should be stored in :attr:DataFrame.attrs.

Examples
>>> df = pd.DataFrame({"A": [1, 2]})>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False
method

swapaxes(axis1, axis2, copy=True)

Interchange axes and swap values axes appropriately.

method

droplevel(level, axis=0)

Return Series/DataFrame with requested index / column level(s) removed.

Parameters
  • level (int, str, or list-like) If a string is given, must be the name of a levelIf list-like, elements must be names or positional indexes of levels.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis along which the level(s) is removed:
    • 0 or 'index': remove level(s) in column.
    • 1 or 'columns': remove level(s) in row.
Returns (Series/DataFrame)

Series/DataFrame with requested index / column level(s) removed.

Examples
>>> df = pd.DataFrame([...     [1, 2, 3, 4],
...     [5, 6, 7, 8],
...     [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
...     ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1   c   d
level_2   e   f
a b
1 2      3   4
5 6      7   8
9 10    11  12
>>> df.droplevel('a')
level_1   c   d
level_2   e   f
b
2        3   4
6        7   8
10      11  12
>>> df.droplevel('level_2', axis=1)
level_1   c   d
a b
1 2      3   4
5 6      7   8
9 10    11  12
method

squeeze(axis=None)

Squeeze 1 dimensional axis objects into scalars.

Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged.

This method is most useful when you don't know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call squeeze to ensure you have a Series.

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default None) A specific axis to squeeze. By default, all length-1 axes aresqueezed.
Returns (DataFrame, Series, or scalar)

The projection after squeezing axis or all the axes.

See Also

Series.iloc : Integer-location based indexing for selecting scalars.DataFrame.iloc : Integer-location based indexing for selecting Series. Series.to_frame : Inverse of DataFrame.squeeze for a single-column DataFrame.

Examples
>>> primes = pd.Series([2, 3, 5, 7])

Slicing might produce a Series with a single value:

>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0    2
dtype: int64
>>> even_primes.squeeze()
2

Squeezing objects with more than one value in every axis does nothing:

>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1    3
2    5
3    7
dtype: int64
>>> odd_primes.squeeze()
1    3
2    5
3    7
dtype: int64

Squeezing is even more effective when used with DataFrames.

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
   a  b
0  1  2
1  3  4

Slicing a single column will produce a DataFrame with the columns having only one value:

>>> df_a = df[['a']]
>>> df_a
   a
0  1
1  3

So the columns can be squeezed down, resulting in a Series:

>>> df_a.squeeze('columns')
0    1
1    3
Name: a, dtype: int64

Slicing a single row from a single column will produce a single scalar DataFrame:

>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
   a
0  1

Squeezing the rows produces a single scalar Series:

>>> df_0a.squeeze('rows')
a    1
Name: 0, dtype: int64

Squeezing all axes will project directly into a scalar:

>>> df_0a.squeeze()
1
method

rename_axis(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False)

Set the name of the axis for the index or columns.

Parameters
  • mapper (scalar, list-like, optional) Value to set the axis name attribute.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to rename.
  • copy (bool, default True) Also copy underlying data.
  • inplace (bool, default False) Modifies the object directly, instead of creating a new Seriesor DataFrame.
Returns (Series, DataFrame, or None)

The same type as the caller or None if inplace=True.

See Also

Series.rename : Alter Series index labels or name.DataFrame.rename : Alter DataFrame index labels or name. Index.rename : Set new names on index.

Notes

DataFrame.rename_axis supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)
  • (mapper, axis={'index', 'columns'}, ...)

The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter copy is ignored.

The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.

We highly recommend using keyword arguments to clarify your intent.

Examples

Series

>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0       dog
1       cat
2    monkey
dtype: object
>>> s.rename_axis("animal")
animal
0    dog
1    cat
2    monkey
dtype: object

DataFrame

>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
...                    "num_arms": [0, 0, 2]},
...                   ["dog", "cat", "monkey"])
>>> df
        num_legs  num_arms
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("animal")
>>> df
        num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs   num_legs  num_arms
animal
dog            4         0
cat            4         0
monkey         2         2

MultiIndex

>>> df.index = pd.MultiIndex.from_product([['mammal'],
...                                        ['dog', 'cat', 'monkey']],
...                                       names=['type', 'name'])
>>> df
limbs          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(index={'type': 'class'})
limbs          num_legs  num_arms
class  name
mammal dog            4         0
       cat            4         0
       monkey         2         2
>>> df.rename_axis(columns=str.upper)
LIMBS          num_legs  num_arms
type   name
mammal dog            4         0
       cat            4         0
       monkey         2         2
method

equals(other)

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype.

Parameters
  • other (Series or DataFrame) The other Series or DataFrame to be compared with the first.
Returns (bool)

True if all elements are the same in both objects, Falseotherwise.

See Also

Series.eq : Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise. DataFrame.eq : Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise. testing.assert_series_equal : Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others. testing.assert_frame_equal : Like assert_series_equal, but targets DataFrames. numpy.array_equal : Return True if two arrays have the same shape and elements, False otherwise.

Examples
>>> df = pd.DataFrame({1: [10], 2: [20]})>>> df
    1   2
0  10  20

DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True.

>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
    1   2
0  10  20
>>> df.equals(exactly_equal)
True

DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True.

>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
   1.0  2.0
0   10   20
>>> df.equals(different_column_type)
True

DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types.

>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
      1     2
0  10.0  20.0
>>> df.equals(different_data_type)
False
method

bool()

Return the bool of a single element Series or DataFrame.

This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception).

Returns (bool)

The value in the Series or DataFrame.

See Also

Series.astype : Change the data type of a Series, including to boolean.DataFrame.astype : Change the data type of a DataFrame, including to boolean. numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.

Examples

The method will only work for single element objects with a boolean value:

>>> pd.Series([True]).bool()
True
>>> pd.Series([False]).bool()
False
>>> pd.DataFrame({'col': [True]}).bool()
True
>>> pd.DataFrame({'col': [False]}).bool()
False
method

__iter__()

Iterate over info axis.

Returns (iterator)

Info axis as iterator.

method

keys()

Get the 'info axis' (see Indexing for more).

This is index for Series, columns for DataFrame.

Returns (Index)

Info axis.

method

__contains__(key) → bool

True if the key is in the info axis

method

__array_wrap__(result, context=None)

Gets called after a ufunc and other functions.

Parameters
  • result (np.ndarray) The result of the ufunc or other function called on the NumPy arrayreturned by array
  • context (tuple of (func, tuple, int)) This parameter is returned by ufuncs as a 3-element tuple: (name of theufunc, arguments of the ufunc, domain of the ufunc), but is not set by other numpy functions.q

Notes

Series implements __array_ufunc_ so this not called for ufunc on Series.

method

to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None, storage_options=None)

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters
  • excel_writer (path-like, file-like, or ExcelWriter object) File path or existing ExcelWriter.
  • sheet_name (str, default 'Sheet1') Name of sheet which will contain DataFrame.
  • na_rep (str, default '') Missing data representation.
  • float_format (str, optional) Format string for floating point numbers. For examplefloat_format="%.2f" will format 0.1234 to 0.12.
  • columns (sequence or list of str, optional) Columns to write.
  • header (bool or list of str, default True) Write out the column names. If a list of string is given it isassumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • index_label (str or sequence, optional) Column label for index column(s) if desired. If not specified, andheader and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
  • startrow (int, default 0) Upper left cell row to dump data frame.
  • startcol (int, default 0) Upper left cell column to dump data frame.
  • engine (str, optional) Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set thisvia the options io.excel.xlsx.writer, io.excel.xls.writer, and io.excel.xlsm.writer.
    .. deprecated:: 1.2.0
    As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
    maintained, the ``xlwt`` engine will be removed in a future version
    of pandas.
    
  • merge_cells (bool, default True) Write MultiIndex and Hierarchical Rows as merged cells.
  • encoding (str, optional) Encoding of the resulting excel file. Only necessary for xlwt,other writers support unicode natively.
  • inf_rep (str, default 'inf') Representation for infinity (there is no native representation forinfinity in Excel).
  • verbose (bool, default True) Display more information in the error logs.
  • freeze_panes (tuple of int (length 2), optional) Specifies the one-based bottommost row and rightmost column thatis to be frozen.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
See Also

to_csv : Write DataFrame to a comma-separated values (csv) file.ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame.

Notes

For compatibility with :meth:~DataFrame.to_csv, to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook.

Examples

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method

to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=True, indent=None, storage_options=None)

Convert the object to a JSON string.

Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Parameters
  • path_or_buf (str or file handle, optional) File path or object. If not specified, the result is returned asa string.
  • orient (str) Indication of expected JSON string format.
    • Series:
      • default is 'index'
      • allowed values are: {'split', 'records', 'index', 'table'}.
    • DataFrame:
      • default is 'columns'
      • allowed values are: {'split', 'records', 'index', 'columns', 'values', 'table'}.
    • The format of the JSON string:
      • 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
      • 'records' : list like [{column -> value}, ... , {column -> value}]
      • 'index' : dict like {index -> {column -> value}}
      • 'columns' : dict like {column -> {index -> value}}
      • 'values' : just the values array
      • 'table' : dict like {'schema': {schema}, 'data': {data}}
      Describing the data, where data component is like orient='records'.
  • date_format ({None, 'epoch', 'iso'}) Type of date conversion. 'epoch' = epoch milliseconds,'iso' = ISO8601. The default depends on the orient. For orient='table', the default is 'iso'. For all other orients, the default is 'epoch'.
  • double_precision (int, default 10) The number of decimal places to use when encodingfloating point values.
  • force_ascii (bool, default True) Force encoded string to be ASCII.
  • date_unit (str, default 'ms' (milliseconds)) The time unit to encode to, governs timestamp and ISO8601precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively.
  • default_handler (callable, default None) Handler to call if object cannot otherwise be converted to asuitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
  • lines (bool, default False) If 'orient' is 'records' write out line-delimited json format. Willthrow ValueError if incorrect 'orient' since others are not list-like.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}) ,e .
  • index (bool, default True) Whether to include the index values in the JSON string. Notincluding the index (index=False) is only supported when orient is 'split' or 'table'.
  • indent (int, optional) Length of whitespace used to indent each record.
    .. versionadded:: 1.0.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
Returns (None or str)

If path_or_buf is None, returns the resulting json format as astring. Otherwise returns None.

See Also

read_json : Convert a JSON string to pandas object.

Notes

The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release.

orient='table' contains a 'pandas_version' field under 'schema'. This stores the version of pandas used in the latest revision of the schema.

Examples
>>> import json>>> df = pd.DataFrame(
...     [["a", "b"], ["c", "d"]],
...     index=["row 1", "row 2"],
...     columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> result = df.to_json(orient="records")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
[
    {
        "col 1": "a",
        "col 2": "b"
    },
    {
        "col 1": "c",
        "col 2": "d"
    }
]

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> result = df.to_json(orient="index")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "row 1": {
        "col 1": "a",
        "col 2": "b"
    },
    "row 2": {
        "col 1": "c",
        "col 2": "d"
    }
}

Encoding/decoding a Dataframe using 'columns' formatted JSON:

>>> result = df.to_json(orient="columns")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "col 1": {
        "row 1": "a",
        "row 2": "c"
    },
    "col 2": {
        "row 1": "b",
        "row 2": "d"
    }
}

Encoding/decoding a Dataframe using 'values' formatted JSON:

>>> result = df.to_json(orient="values")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
[
    [
        "a",
        "b"
    ],
    [
        "c",
        "d"
    ]
]

Encoding with Table Schema:

>>> result = df.to_json(orient="table")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)  # doctest: +SKIP
{
    "schema": {
        "fields": [
            {
                "name": "index",
                "type": "string"
            },
            {
                "name": "col 1",
                "type": "string"
            },
            {
                "name": "col 2",
                "type": "string"
            }
        ],
        "primaryKey": [
            "index"
        ],
        "pandas_version": "0.20.0"
    },
    "data": [
        {
            "index": "row 1",
            "col 1": "a",
            "col 2": "b"
        },
        {
            "index": "row 2",
            "col 1": "c",
            "col 2": "d"
        }
    ]
}
method

to_hdf(path_or_buf, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')

Write the contained data to an HDF5 file using HDFStore.

Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects.

In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.

.. warning::

One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.

For more information see the :ref:user guide <io.hdf5>.

Parameters
  • path_or_buf (str or pandas.HDFStore) File path or HDFStore object.
  • key (str) Identifier for the group in the store.
  • mode ({'a', 'w', 'r+'}, default 'a') Mode to open file:
    • 'w': write, a new file is created (an existing file with the same name would be deleted).
    • 'a': append, an existing file is opened for reading and writing, and if the file does not exist it is created.
    • 'r+': similar to 'a', but the file must already exist.
  • complevel ({0-9}, optional) Specifies a compression level for data.A value of 0 disables compression.
  • complib ({'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib') Specifies the compression library to be used.As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError.
  • append (bool, default False) For Table formats, append the input data to the existing.
  • format ({'fixed', 'table', None}, default 'fixed') Possible values:
    • 'fixed': Fixed format. Fast writing/reading. Not-appendable, nor searchable.
    • 'table': Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data.
    • If None, pd.get_option('io.hdf.default_format') is checked, followed by fallback to "fixed"
  • min_itemsize (dict or int, optional) Map column names to minimum string sizes for columns.
  • nan_rep (Any, optional) How to represent null values as str.Not allowed with append=True.
  • data_columns (list of columns or True, optional) List of columns to create as indexed data columns for on-diskqueries, or True to use all columns. By default only the axes of the object are indexed. See :ref:io.hdf5-query-data-columns. Applicable only to format='table'.
  • errors (str, default 'strict') Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:open for a full list of options.
See Also

read_hdf : Read from HDF file.DataFrame.to_parquet : Write a DataFrame to the binary parquet format. DataFrame.to_sql : Write to a SQL table. DataFrame.to_feather : Write out feather-format for DataFrames. DataFrame.to_csv : Write out to a csv file.

Examples
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},...                   index=['a', 'b', 'c'])
>>> df.to_hdf('data.h5', key='df', mode='w')

We can add another object to the same file:

>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf('data.h5', key='s')

Reading from HDF file:

>>> pd.read_hdf('data.h5', 'df')
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')
0    1
1    2
2    3
3    4
dtype: int64

Deleting file with data:

>>> import os
>>> os.remove('data.h5')
method

to_sql(name, con, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None, method=None)

Write records stored in a DataFrame to a SQL database.

Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten.

Parameters
  • name (str) Name of SQL table.
  • con (sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection) Using SQLAlchemy makes it possible to use any DB supported by thatlibrary. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See here <https://docs.sqlalchemy.org/en/13/core/connections.html>_.
  • schema (str, optional) Specify the schema (if database flavor supports this). If None, usedefault schema.
  • if_exists ({'fail', 'replace', 'append'}, default 'fail') How to behave if the table already exists.
    • fail: Raise a ValueError.
    • replace: Drop the table before inserting new values.
    • append: Insert new values to the existing table.
  • index (bool, default True) Write DataFrame index as a column. Uses index_label as the columnname in the table.
  • index_label (str or sequence, default None) Column label for index column(s). If None is given (default) andindex is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.
  • chunksize (int, optional) Specify the number of rows in each batch to be written at a time.By default, all rows will be written at once.
  • dtype (dict or scalar, optional) Specifying the datatype for columns. If a dictionary is used, thekeys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns.
  • method ({None, 'multi', callable}, optional) Controls the SQL insertion clause used:
    • None : Uses standard SQL INSERT clause (one per row).
    • 'multi': Pass multiple values in a single INSERT clause.
    • callable with signature (pd_table, conn, keys, data_iter).
    Details and a sample callable implementation can be found in the section :ref:insert method <io.sql.method>.
Raises
  • ValueError When the table already exists and if_exists is 'fail' (thedefault).
See Also

read_sql : Read a DataFrame from a table.

Notes

Timezone aware datetime columns will be written as Timestamp with timezone type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone.

References

.. [1] https://docs.sqlalchemy.org.. [2] https://www.python.org/dev/peps/pep-0249/

Examples

Create an in-memory SQLite database.

>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)

Create a table from scratch with 3 rows.

>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
     name
0  User 1
1  User 2
2  User 3
>>> df.to_sql('users', con=engine)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]

An sqlalchemy.engine.Connection can also be passed to con:

>>> with engine.begin() as connection:
...     df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
...     df1.to_sql('users', con=connection, if_exists='append')

This is allowed to support operations that require that the same DBAPI connection is used for the entire operation.

>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql('users', con=engine, if_exists='append')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
 (0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
 (1, 'User 7')]

Overwrite the table with just df2.

>>> df2.to_sql('users', con=engine, if_exists='replace',
...            index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 6'), (1, 'User 7')]

Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars.

>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
     A
0  1.0
1  NaN
2  2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql('integers', con=engine, index=False,
...           dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall()
[(1,), (None,), (2,)]
method

to_pickle(path, compression='infer', protocol=5, storage_options=None)

Pickle (serialize) object to file.

Parameters
  • path (str) File path where the pickled object will be stored.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') A string representing the compression to use in the output file. Bydefault, infers from the file extension in specified path. Compression mode may be any of the following possible values: {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}. If compression mode is ‘infer’ and path_or_buf is path-like, then detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression). If dict given and mode is ‘zip’ or inferred as ‘zip’, other entries passed as additional compression options.
  • protocol (int) Int which indicates which protocol should be used by the pickler,default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
    .. [1] https://docs.python.org/3/library/pickle.html.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
See Also

read_pickle : Load pickled pandas object (or any object) from file.DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Examples
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
>>> import os
>>> os.remove("./dummy.pkl")
method

to_clipboard(excel=True, sep=None, **kwargs)

Copy object to the system clipboard.

Write a text representation of object to the system clipboard. This can be pasted into Excel, for example.

Parameters
  • excel (bool, default True) Produce output in a csv format for easy pasting into excel.
    • True, use the provided separator for csv pasting.
    • False, write a string representation of the object to the clipboard.
  • sep (str, default ``'\t'``) Field delimiter.
  • **kwargs These parameters will be passed to DataFrame.to_csv.
See Also

DataFrame.to_csv : Write a DataFrame to a comma-separated values (csv) file. read_clipboard : Read text from clipboard and pass to read_table.

Notes

Requirements for your platform.

  • Linux : xclip, or xsel (with PyQt4 modules)
  • Windows : none
  • OS X : none
Examples

Copy the contents of a DataFrame to the clipboard.

>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',')  # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6

We can omit the index by passing the keyword index and setting it to false.

>>> df.to_clipboard(sep=',', index=False)  # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
method

to_xarray()

Return an xarray object from the pandas object.

Returns (xarray.DataArray or xarray.Dataset)

Data in the pandas structure converted to Dataset if the object isa DataFrame, or a DataArray if the object is a Series.

See Also

DataFrame.to_hdf : Write DataFrame to an HDF5 file.DataFrame.to_parquet : Write a DataFrame to the binary parquet format.

Notes

See the xarray docs <https://xarray.pydata.org/en/stable/>__

Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),...                    ('parrot', 'bird', 24.0, 2),
...                    ('lion', 'mammal', 80.5, 4),
...                    ('monkey', 'mammal', np.nan, 4)],
...                   columns=['name', 'class', 'max_speed',
...                            'num_legs'])
>>> df
     name   class  max_speed  num_legs
0  falcon    bird      389.0         2
1  parrot    bird       24.0         2
2    lion  mammal       80.5         4
3  monkey  mammal        NaN         4
>>> df.to_xarray()
<xarray.Dataset>
Dimensions:    (index: 4)
Coordinates:
  * index      (index) int64 0 1 2 3
Data variables:
    name       (index) object 'falcon' 'parrot' 'lion' 'monkey'
    class      (index) object 'bird' 'bird' 'mammal' 'mammal'
    max_speed  (index) float64 389.0 24.0 80.5 nan
    num_legs   (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. ,  24. ,  80.5,   nan])
Coordinates:
  * index    (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
...                         '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
...                               'animal': ['falcon', 'parrot',
...                                          'falcon', 'parrot'],
...                               'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
                   speed
date       animal
2018-01-01 falcon    350
           parrot     18
2018-01-02 falcon    361
           parrot     15
>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions:  (animal: 2, date: 2)
Coordinates:
  * date     (date) datetime64[ns] 2018-01-01 2018-01-02
  * animal   (animal) object 'falcon' 'parrot'
Data variables:
    speed    (date, animal) int64 350 18 361 15
method

to_latex(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, position=None)

Render object to a LaTeX tabular, longtable, or nested table/tabular.

Requires \usepackage{booktabs}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{table.tex}.

.. versionchanged:: 1.0.0 Added caption and label arguments.

.. versionchanged:: 1.2.0 Added position argument, changed meaning of caption argument.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (list of label, optional) The subset of columns to write. Writes all columns by default.
  • col_space (int, optional) The minimum width of each column.
  • header (bool or list of str, default True) Write out the column names. If a list of strings is given,it is assumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • na_rep (str, default 'NaN') Missing data representation.
  • formatters (list of functions or dict of {str: function}, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List must be of length equal to the number of columns.
  • float_format (one-parameter function or str, optional, default None) Formatter for floating point numbers. For examplefloat_format="%.2f" and float_format="{:0.2f}".format will both result in 0.1234 being formatted as 0.12.
  • sparsify (bool, optional) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row. By default, the value will be read from the config module.
  • index_names (bool, default True) Prints the names of the indexes.
  • bold_rows (bool, default False) Make the row labels bold in the output.
  • column_format (str, optional) The columns format as specified in LaTeX table format<https://en.wikibooks.org/wiki/LaTeX/Tables>__ e.g. 'rcl' for 3 columns. By default, 'l' will be used for all columns except columns of numbers, which default to 'r'.
  • longtable (bool, optional) By default, the value will be read from the pandas configmodule. Use a longtable environment instead of tabular. Requires adding a \usepackage{longtable} to your LaTeX preamble.
  • escape (bool, optional) By default, the value will be read from the pandas configmodule. When set to False prevents from escaping latex special characters in column names.
  • encoding (str, optional) A string representing the encoding to use in the output file,defaults to 'utf-8'.
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • multicolumn (bool, default True) Use \multicolumn to enhance MultiIndex columns.The default will be read from the config module.
  • multicolumn_format (str, default 'l') The alignment for multicolumns, similar to column_formatThe default will be read from the config module.
  • multirow (bool, default False) Use \multirow to enhance MultiIndex rows. Requires adding a\usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module.
  • caption (str or tuple, optional) Tuple (full_caption, short_caption),which results in \caption[short_caption]{full_caption}; if a single string is passed, no short caption will be set.
    .. versionadded:: 1.0.0
    .. versionchanged:: 1.2.0 Optionally allow caption to be a tuple (full_caption, short_caption).
  • label (str, optional) The LaTeX label to be placed inside \label{} in the output.This is used with \ref{} in the main .tex file.
    .. versionadded:: 1.0.0
  • position (str, optional) The LaTeX positional argument for tables, to be placed after\begin{} in the output.
    .. versionadded:: 1.2.0
    Returns
    -------
    str or None
        If buf is None, returns the result as a string. Otherwise returns
        None.
    
See Also

DataFrame.to_string : Render a DataFrame to a console-friendly tabular output. DataFrame.to_html : Render a DataFrame as an HTML table.

Examples
>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],...                   mask=['red', 'purple'],
...                   weapon=['sai', 'bo staff']))
>>> print(df.to_latex(index=False))  # doctest: +NORMALIZE_WHITESPACE
\begin{tabular}{lll}
 \toprule
       name &    mask &    weapon \\
 \midrule
    Raphael &     red &       sai \\
  Donatello &  purple &  bo staff \\
\bottomrule
\end{tabular}
method

to_csv(path_or_buf=None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.', errors='strict', storage_options=None)

Write object to a comma-separated values (csv) file.

Parameters
  • path_or_buf (str or file handle, default None) File path or object, if None is provided the result is returned asa string. If a non-binary file object is passed, it should be opened with newline='', disabling universal newlines. If a binary file object is passed, mode might need to contain a 'b'.
    .. versionchanged:: 1.2.0
    Support for binary file objects was introduced.
  • sep (str, default ',') String of length 1. Field delimiter for the output file.
  • na_rep (str, default '') Missing data representation.
  • float_format (str, default None) Format string for floating point numbers.
  • columns (sequence, optional) Columns to write.
  • header (bool or list of str, default True) Write out the column names. If a list of strings is given it isassumed to be aliases for the column names.
  • index (bool, default True) Write row names (index).
  • index_label (str or sequence, or False, default None) Column label for index column(s) if desired. If None is given, andheader and index are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R.
  • mode (str) Python write mode, default 'w'.
  • encoding (str, optional) A string representing the encoding to use in the output file,defaults to 'utf-8'. encoding is not supported if path_or_buf is a non-binary file object.
  • compression (str or dict, default 'infer') If str, represents compression mode. If dict, value at 'method' isthe compression mode. Compression mode may be any of the following possible values: {'infer', 'gzip', 'bz2', 'zip', 'xz', None}. If compression mode is 'infer' and path_or_buf is path-like, then detect compression mode from the following extensions: '.gz', '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given and mode is one of {'zip', 'gzip', 'bz2'}, or inferred as one of the above, other entries passed as additional compression options.
    .. versionchanged:: 1.0.0
    May now be a dict with key 'method' as compression mode and other entries as additional compression options if compression mode is 'zip'.
    .. versionchanged:: 1.1.0
    Passing compression options as keys in dict is supported for compression modes 'gzip' and 'bz2' as well as 'zip'.
    .. versionchanged:: 1.2.0
    Compression is supported for binary file objects.
    
    .. versionchanged:: 1.2.0
    Previous versions forwarded dict entries for 'gzip' to
    `gzip.open` instead of `gzip.GzipFile` which prevented
    setting `mtime`.
    
  • quoting (optional constant from csv module) Defaults to csv.QUOTE_MINIMAL. If you have set a float_formatthen floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric.
  • quotechar (str, default '\"') String of length 1. Character used to quote fields.
  • line_terminator (str, optional) The newline character or character sequence to use in the outputfile. Defaults to os.linesep, which depends on the OS in which this method is called ('\n' for linux, '\r\n' for Windows, i.e.).
  • chunksize (int or None) Rows to write at a time.
  • date_format (str, default None) Format string for datetime objects.
  • doublequote (bool, default True) Control quoting of quotechar inside a field.
  • escapechar (str, default None) String of length 1. Character used to escape sep and quotecharwhen appropriate.
  • decimal (str, default '.') Character recognized as decimal separator. E.g. use ',' forEuropean data.
  • errors (str, default 'strict') Specifies how encoding and decoding errors are to be handled.See the errors argument for :func:open for a full list of options.
    .. versionadded:: 1.1.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
Returns (None or str)

If path_or_buf is None, returns the resulting csv format as astring. Otherwise returns None.

See Also

read_csv : Load a CSV file into a DataFrame.to_excel : Write DataFrame to an Excel file.

Examples
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'

Create 'out.zip' containing 'out.csv'

>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  # doctest: +SKIP
method

take(indices, axis=0, is_copy=None, **kwargs)

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

Parameters
  • indices (array-like) An array of ints indicating which positions to take.
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) The axis on which to select elements. 0 means that we areselecting rows, 1 means that we are selecting columns.
  • is_copy (bool) Before pandas 1.0, is_copy=False can be specified to ensurethat the return value is an actual copy. Starting with pandas 1.0, take always returns a copy, and the keyword is therefore deprecated.
    .. deprecated:: 1.0.0
  • **kwargs For compatibility with :meth:numpy.take. Has no effect on theoutput.
Returns (taken : same type as caller)

An array-like containing the elements taken from the object.

See Also

DataFrame.loc : Select a subset of a DataFrame by labels.DataFrame.iloc : Select a subset of a DataFrame by positions. numpy.take : Take elements from an array along an axis.

Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=['name', 'class', 'max_speed'],
...                   index=[0, 2, 3, 1])
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That's because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
     name   class  max_speed
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5
method

xs(key, axis=0, level=None, drop_level=True)

Return cross-section from the Series/DataFrame.

This method takes a key argument to select data at a particular level of a MultiIndex.

Parameters
  • key (label or tuple of label) Label contained in the index, or partially in a MultiIndex.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis to retrieve cross-section on.
  • level (object, defaults to first n levels (n=1 or len(key))) In case of a key partially contained in a MultiIndex, indicatewhich levels are used. Levels can be referred by label or position.
  • drop_level (bool, default True) If False, returns object with same levels as self.
Returns (Series or DataFrame)

Cross-section from the original Series or DataFramecorresponding to the selected index levels.

See Also

DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc : Purely integer-location based indexing for selection by position.

Notes

xs can not be used to set values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see :ref:MultiIndex Slicers <advanced.mi_slicers>.

Examples
>>> d = {'num_legs': [4, 4, 2, 2],...      'num_wings': [0, 0, 2, 2],
...      'class': ['mammal', 'mammal', 'mammal', 'bird'],
...      'animal': ['cat', 'dog', 'bat', 'penguin'],
...      'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
                           num_legs  num_wings
class  animal  locomotion
mammal cat     walks              4          0
       dog     walks              4          0
       bat     flies              2          2
bird   penguin walks              2          2

Get values at specified index

>>> df.xs('mammal')
                   num_legs  num_wings
animal locomotion
cat    walks              4          0
dog    walks              4          0
bat    flies              2          2

Get values at several indexes

>>> df.xs(('mammal', 'dog'))
            num_legs  num_wings
locomotion
walks              4          0

Get values at specified index and level

>>> df.xs('cat', level=1)
                   num_legs  num_wings
class  locomotion
mammal walks              4          0

Get values at several indexes and levels

>>> df.xs(('bird', 'walks'),
...       level=[0, 'locomotion'])
         num_legs  num_wings
animal
penguin         2          2

Get values at specified column and axis

>>> df.xs('num_wings', axis=1)
class   animal   locomotion
mammal  cat      walks         0
        dog      walks         0
        bat      flies         2
bird    penguin  walks         2
Name: num_wings, dtype: int64
method

__delitem__(key)

Delete item

method

get(key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

method

reindex_like(other, method=None, copy=True, limit=None, tolerance=None)

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False.

Parameters
  • other (Object of the same data type) Its row and column indices are used to define the new indicesof this object.
  • method ({None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}) Method to use for filling holes in reindexed DataFrame.Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.
    • None (default): don't fill gaps
    • pad / ffill: propagate last valid observation forward to next valid
    • backfill / bfill: use next valid observation to fill gap
    • nearest: use nearest valid observations to fill gap.
  • copy (bool, default True) Return a new object, even if the passed indexes are the same.
  • limit (int, default None) Maximum number of consecutive labels to fill for inexact matches.
  • tolerance (optional) Maximum distance between original and new labels for inexactmatches. The values of the index at the matching locations must satisfy the equation abs(index[indexer] - target) <= tolerance.
    Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type.
Returns (Series or DataFrame)

Same type as caller, but with changed indices on each axis.

See Also

DataFrame.set_index : Set row labels.DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices.

Notes

Same as calling .reindex(index=other.index, columns=other.columns,...).

Examples
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],...                     [31, 87.8, 'high'],
...                     [22, 71.6, 'medium'],
...                     [35, 95, 'medium']],
...                    columns=['temp_celsius', 'temp_fahrenheit',
...                             'windspeed'],
...                    index=pd.date_range(start='2014-02-12',
...                                        end='2014-02-15', freq='D'))
>>> df1
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium
>>> df2 = pd.DataFrame([[28, 'low'],
...                     [30, 'low'],
...                     [35.1, 'medium']],
...                    columns=['temp_celsius', 'windspeed'],
...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
...                                            '2014-02-15']))
>>> df2
            temp_celsius windspeed
2014-02-12          28.0       low
2014-02-13          30.0       low
2014-02-15          35.1    medium
>>> df2.reindex_like(df1)
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          28.0              NaN       low
2014-02-13          30.0              NaN       low
2014-02-14           NaN              NaN       NaN
2014-02-15          35.1              NaN    medium
method

add_prefix(prefix)

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters
  • prefix (str) The string to add before each label.
Returns (Series or DataFrame)

New Series or DataFrame with updated labels.

See Also

Series.add_suffix: Suffix row labels with string suffix.DataFrame.add_suffix: Suffix column labels with string suffix.

Examples
>>> s = pd.Series([1, 2, 3, 4])>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_prefix('item_')
item_0    1
item_1    2
item_2    3
item_3    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_prefix('col_')
     col_A  col_B
0       1       3
1       2       4
2       3       5
3       4       6
method

add_suffix(suffix)

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Parameters
  • suffix (str) The string to add after each label.
Returns (Series or DataFrame)

New Series or DataFrame with updated labels.

See Also

Series.add_prefix: Prefix row labels with string prefix.DataFrame.add_prefix: Prefix column labels with string prefix.

Examples
>>> s = pd.Series([1, 2, 3, 4])>>> s
0    1
1    2
2    3
3    4
dtype: int64
>>> s.add_suffix('_item')
0_item    1
1_item    2
2_item    3
3_item    4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
   A  B
0  1  3
1  2  4
2  3  5
3  4  6
>>> df.add_suffix('_col')
     A_col  B_col
0       1       3
1       2       4
2       3       5
3       4       6
method

filter(items=None, like=None, regex=None, axis=None)

Subset the dataframe rows or columns according to the specified index labels.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

Parameters
  • items (list-like) Keep labels from axis which are in items.
  • like (str) Keep labels from axis for which "like in label == True".
  • regex (str (regular expression)) Keep labels from axis for which re.search(regex, label) == True.
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) The axis to filter on, expressed either as an index (int)or axis name (str). By default this is the info axis, 'index' for Series, 'columns' for DataFrame.
See Also

DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array.

Notes

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

Examples
>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])
>>> df
        one  two  three
mouse     1    2      3
rabbit    4    5      6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
         one  three
mouse     1      3
rabbit    4      6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6
method

head(n=5)

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last n rows, equivalent to df[:-n].

Parameters
  • n (int, default 5) Number of rows to select.
Returns (same type as caller)

The first n rows of the caller object.

See Also

DataFrame.tail: Returns the last n rows.

Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the first 5 lines

>>> df.head()
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey

Viewing the first n lines (three in this case)

>>> df.head(3)
      animal
0  alligator
1        bee
2     falcon

For negative values of n

>>> df.head(-3)
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
method

tail(n=5)

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first n rows, equivalent to df[n:].

Parameters
  • n (int, default 5) Number of rows to select.
Returns (type of caller)

The last n rows of the caller object.

See Also

DataFrame.head : The first n rows of the caller object.

Examples
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',...                    'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
      animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra

Viewing the last 5 lines

>>> df.tail()
   animal
4  monkey
5  parrot
6   shark
7   whale
8   zebra

Viewing the last n lines (three in this case)

>>> df.tail(3)
  animal
6  shark
7  whale
8  zebra

For negative values of n

>>> df.tail(-3)
   animal
3    lion
4  monkey
5  parrot
6   shark
7   whale
8   zebra
method

sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ignore_index=False)

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters
  • n (int, optional) Number of items from axis to return. Cannot be used with frac.Default = 1 if frac = None.
  • frac (float, optional) Fraction of axis items to return. Cannot be used with n.
  • replace (bool, default False) Allow or disallow sampling of the same row more than once.
  • weights (str or ndarray-like, optional) Default 'None' results in equal probability weighting.If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed.
  • random_state (int, array-like, BitGenerator, np.random.RandomState, optional) If int, array-like, or BitGenerator (NumPy>=1.17), seed forrandom number generator If np.random.RandomState, use as numpy RandomState object.
    .. versionchanged:: 1.1.0
    array-like and BitGenerator (for NumPy>=1.17) object now passed to
    np.random.RandomState() as seed
    
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default None) Axis to sample. Accepts axis number or name. Default is stat axisfor given data type (0 for Series and DataFrames).
  • ignore_index (bool, default False) If True, the resulting index will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.3.0
Returns (Series or DataFrame)

A new object of same type as caller containing n items randomlysampled from the caller object.

See Also

DataFrameGroupBy.sample: Generates random samples from each group of a DataFrame object. SeriesGroupBy.sample: Generates random samples from each group of a Series object. numpy.random.choice: Generates a random sample from a given 1-D numpy array.

Notes

If frac > 1, replacement should be set to True.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],...                    'num_wings': [2, 0, 0, 0],
...                    'num_specimen_seen': [10, 2, 1, 8]},
...                   index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

>>> df['num_legs'].sample(n=3, random_state=1)
fish      0
spider    8
falcon    2
Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

>>> df.sample(frac=0.5, replace=True, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8

An upsample sample of the DataFrame with replacement: Note that replace parameter has to be True for frac parameter > 1.

>>> df.sample(frac=2, replace=True, random_state=1)
        num_legs  num_wings  num_specimen_seen
dog            4          0                  2
fish           0          0                  8
falcon         2          2                 10
falcon         2          2                 10
fish           0          0                  8
dog            4          0                  2
fish           0          0                  8
dog            4          0                  2

Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled.

>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
fish           0          0                  8
method

pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs).

Parameters
  • func (function) Function to apply to the Series/DataFrame.args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the Series/DataFrame.
See Also

DataFrame.apply : Apply a function along input axis of DataFrame.DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Series.map : Apply a mapping correspondence on a :class:~pandas.Series.

Notes

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing

>>> func(g(h(df), arg1=a), arg2=b, arg3=c)  # doctest: +SKIP

You can write

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe(func, arg2=b, arg3=c)
... )  # doctest: +SKIP

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose f takes its data as arg2:

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe((func, 'arg2'), arg1=a, arg3=c)
...  )  # doctest: +SKIP
method

__finalize__(other, method=None, **kwargs) → FrameOrSeries

Propagate metadata from other to self.

Parameters
  • other (the object from which to get the attributes that we are going) to propagate
  • method (str, optional) A passed method name providing context on where __finalize__was called.
    .. warning::
    The value passed as method are not currently considered stable across pandas releases.
method

__getattr__(name)

After regular attribute access, try looking up the nameThis allows simpler access to columns for interactive use.

method

__setattr__(name, value)

After regular attribute access, try setting the nameThis allows simpler access to columns for interactive use.

method

astype(dtype, copy=True, errors='raise')

Cast a pandas object to a specified dtype dtype.

Parameters
  • dtype (data type, or dict of column name -> data type) Use a numpy.dtype or Python type to cast entire pandas object tothe same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types.
  • copy (bool, default True) Return a copy when copy=True (be very careful settingcopy=False as changes to values then may propagate to other pandas objects).
  • errors ({'raise', 'ignore'}, default 'raise') Control raising of exceptions on invalid data for provided dtype.
    • raise : allow exceptions to be raised
    • ignore : suppress exceptions. On error return original object.
See Also

to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type.

Notes

.. deprecated:: 1.3.0

Using ``astype`` to convert from timezone-naive dtype to
timezone-aware dtype is deprecated and will raise in a
future version.  Use :meth:`Series.dt.tz_localize` instead.
Examples

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype('int32').dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({'col1': 'int32'}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype('int64')
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')
0    1
1    2
dtype: category
Categories (2, int64): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(
...     categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Note that using copy=False and changing data on a new pandas object may propagate changes:

>>> s1 = pd.Series([1, 2])
>>> s2 = s1.astype('int64', copy=False)
>>> s2[0] = 10
>>> s1  # note that s1[0] has changed too
0    10
1     2
dtype: int64

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[ns]
method

__deepcopy__(memo=None) → FrameOrSeries

method

infer_objects()

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

See Also

to_datetime : Convert argument to datetime.to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to numeric type. convert_dtypes : Convert argument to best possible dtype.

Examples
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3
>>> df.dtypes
A    object
dtype: object
>>> df.infer_objects().dtypes
A    int64
dtype: object
method

convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True)

Convert columns to best possible dtypes using dtypes supporting pd.NA.

.. versionadded:: 1.0.0

Parameters
  • infer_objects (bool, default True) Whether object dtypes should be converted to the best possible types.
  • convert_string (bool, default True) Whether object dtypes should be converted to StringDtype().
  • convert_integer (bool, default True) Whether, if possible, conversion can be done to integer extension types.
  • convert_boolean (bool, defaults True) Whether object dtypes should be converted to BooleanDtypes().
  • convert_floating (bool, defaults True) Whether, if possible, conversion can be done to floating extension types.If convert_integer is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers.
    .. versionadded:: 1.2.0
Returns (Series or DataFrame)

Copy of input object with new dtype.

See Also

infer_objects : Infer dtypes of objects.to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type.

Notes

By default, convert_dtypes will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support pd.NA. By using the options convert_string, convert_integer, convert_boolean and convert_boolean, it is possible to turn off individual conversions to StringDtype, the integer extension types, BooleanDtype or floating extension types, respectively.

For object-dtyped columns, if infer_objects is True, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to StringDtype, BooleanDtype or an appropriate integer or floating extension type, otherwise leave as object.

If the dtype is integer, convert to an appropriate integer extension type.

If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type.

.. versionchanged:: 1.2 Starting with pandas 1.2, this method also converts float columns to the nullable floating extension type.

In the future, as new dtypes are added that support pd.NA, the results of this method will change to support those new dtypes.

Examples
>>> df = pd.DataFrame(...     {
...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
...         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
...         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
...         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
...         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
...         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
...     }
... )

Start with a DataFrame with default dtypes.

>>> df
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0
>>> df.dtypes
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

Convert the DataFrame to use best possible dtypes.

>>> dfn = df.convert_dtypes()
>>> dfn
   a  b      c     d     e      f
0  1  x   True     h    10   <NA>
1  2  y  False     i  <NA>  100.5
2  3  z   <NA>  <NA>    20  200.0
>>> dfn.dtypes
a      Int32
b     string
c    boolean
d     string
e      Int64
f    Float64
dtype: object

Start with a Series of strings and missing data represented by np.nan.

>>> s = pd.Series(["a", "b", np.nan])
>>> s
0      a
1      b
2    NaN
dtype: object

Obtain a Series with dtype StringDtype.

>>> s.convert_dtypes()
0       a
1       b
2    <NA>
dtype: string
method

ffill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for :meth:DataFrame.fillna with method='ffill'.

Returns (Series/DataFrame or None)

Object with missing values filled or None if inplace=True.

method

bfill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for :meth:DataFrame.fillna with method='bfill'.

Returns (Series/DataFrame or None)

Object with missing values filled or None if inplace=True.

method

asof(where, subset=None)

Return the last row(s) without any NaNs before where.

The last row (for each element in where, if list) without any NaN is taken. In case of a :class:~pandas.DataFrame, the last row without NaN considering only the subset of columns (if not None)

If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame

Parameters
  • where (date or array-like of dates) Date(s) before which the last row(s) are returned.
  • subset (str or array-like of str, default `None`) For DataFrame, if not None, only use these columns tocheck for NaNs.
Returns (scalar, Series, or DataFrame)

:

r , r n e

.

See Also

merge_asof : Perform an asof merge. Similar to left join.

Notes

Dates are assumed to be sorted. Raises if this is not the case.

Examples

A Series and a scalar where.

>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10    1.0
20    2.0
30    NaN
40    4.0
dtype: float64
>>> s.asof(20)
2.0

For a sequence where, a Series is returned. The first value is NaN, because the first element of where is before the first index value.

>>> s.asof([5, 20])
5     NaN
20    2.0
dtype: float64

Missing values are not considered. The following is 2.0, not NaN, even though NaN is at the index location for 30.

>>> s.asof(30)
2.0

Take all columns into consideration

>>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
...                    'b': [None, None, None, None, 500]},
...                   index=pd.DatetimeIndex(['2018-02-27 09:01:00',
...                                           '2018-02-27 09:02:00',
...                                           '2018-02-27 09:03:00',
...                                           '2018-02-27 09:04:00',
...                                           '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']))
                      a   b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN

Take a single column into consideration

>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
...                           '2018-02-27 09:04:30']),
...         subset=['a'])
                         a   b
2018-02-27 09:03:30   30.0 NaN
2018-02-27 09:04:30   40.0 NaN
method

at_time(time, asof=False, axis=None)

Select values at particular time of day (e.g., 9:30AM).

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

between_time : Select values between particular times of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_at_time : Get just the index locations for values at particular time of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='12H')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-09 12:00:00  2
2018-04-10 00:00:00  3
2018-04-10 12:00:00  4
>>> ts.at_time('12:00')
                     A
2018-04-09 12:00:00  2
2018-04-10 12:00:00  4
method

between_time(start_time, end_time, include_start=True, include_end=True, axis=None)

Select values between particular times of the day (e.g., 9:00-9:30 AM).

By setting start_time to be later than end_time, you can get the times that are not between the two times.

Parameters
  • start_time (datetime.time or str) Initial time as a time filter limit.
  • end_time (datetime.time or str) End time as a time filter limit.
  • include_start (bool, default True) Whether the start time needs to be included in the result.
  • include_end (bool, default True) Whether the end time needs to be included in the result.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Determine range time on index or columns value.
Returns (Series or DataFrame)

Data from the original object filtered to the specified dates range.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

at_time : Select values at a particular time of the day.first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
                     A
2018-04-09 00:00:00  1
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3
2018-04-12 01:00:00  4
>>> ts.between_time('0:15', '0:45')
                     A
2018-04-10 00:20:00  2
2018-04-11 00:40:00  3

You get the times that are not between two times by setting start_time later than end_time:

>>> ts.between_time('0:45', '0:15')
                     A
2018-04-09 00:00:00  1
2018-04-12 01:00:00  4
method

first(offset)

Select initial periods of time series data based on a date offset.

When having a DataFrame with dates as index, this function can select the first few rows based on a date offset.

Parameters
  • offset (str, DateOffset or dateutil.relativedelta) The offset length of the data that will be selected. For instance,'1M' will display all the rows having their index within the first month.
Returns (Series or DataFrame)

A subset of the caller.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

last : Select final periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the first 3 days:

>>> ts.first('3D')
            A
2018-04-09  1
2018-04-11  2

Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned.

method

last(offset)

Select final periods of time series data based on a date offset.

For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset.

Parameters
  • offset (str, DateOffset, dateutil.relativedelta) The offset length of the data that will be selected. For instance,'3D' will display all the rows having their index within the last 3 days.
Returns (Series or DataFrame)

A subset of the caller.

Raises
  • TypeError If the index is not a :class:DatetimeIndex
See Also

first : Select initial periods of time series based on a date offset.at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day.

Examples
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
            A
2018-04-09  1
2018-04-11  2
2018-04-13  3
2018-04-15  4

Get the rows for the last 3 days:

>>> ts.last('3D')
            A
2018-04-13  3
2018-04-15  4

Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.

method

rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Index to direct ranking.
  • method ({'average', 'min', 'max', 'first', 'dense'}, default 'average') How to rank the group of records that have the same value (i.e. ties):
    • average: average rank of the group
    • min: lowest rank in the group
    • max: highest rank in the group
    • first: ranks assigned in order they appear in the array
    • dense: like 'min', but rank always increases by 1 between groups.
  • numeric_only (bool, optional) For DataFrame objects, rank only numeric columns if set to True.
  • na_option ({'keep', 'top', 'bottom'}, default 'keep') How to rank NaN values:
    • keep: assign NaN rank to NaN values
    • top: assign lowest rank to NaN values
    • bottom: assign highest rank to NaN values
  • ascending (bool, default True) Whether or not the elements should be ranked in ascending order.
  • pct (bool, default False) Whether or not to display the returned rankings in percentileform.
Returns (same type as caller)

Return a Series or DataFrame with data ranks as values.

See Also

core.groupby.GroupBy.rank : Rank of values within each group.

Examples
>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',...                                    'spider', 'snake'],
...                         'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
    Animal  Number_legs
0      cat          4.0
1  penguin          2.0
2      dog          4.0
3   spider          8.0
4    snake          NaN

The following example shows how the method behaves with the above parameters:

  • default_rank: this is the default behaviour obtained without using any parameter.
  • max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since 'cat' and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
  • NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking.
  • pct_rank: when setting pct = True, the ranking is expressed as percentile rank.
>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
    Animal  Number_legs  default_rank  max_rank  NA_bottom  pct_rank
0      cat          4.0           2.5       3.0        2.5     0.625
1  penguin          2.0           1.0       1.0        1.0     0.250
2      dog          4.0           2.5       3.0        2.5     0.625
3   spider          8.0           4.0       4.0        4.0     1.000
4    snake          NaN           NaN       NaN        5.0       NaN
method

slice_shift(periods=1, axis=0)

Equivalent to shift without copying data.The shifted data will not include the dropped periods and the shifted axis will be smaller than the original.

.. deprecated:: 1.2.0 slice_shift is deprecated, use DataFrame/Series.shift instead.

Parameters
  • periods (int) Number of periods to move, can be positive or negative.

Notes

While the slice_shift is faster than shift, you may pay for it later during alignment.

method

tshift(periods=1, freq=None, axis=0)

Shift the time index, using the index's frequency if available.

.. deprecated:: 1.1.0 Use shift instead.

Parameters
  • periods (int) Number of periods to move, can be positive or negative.
  • freq (DateOffset, timedelta, or str, default None) Increment to use from the tseries moduleor time rule expressed as a string (e.g. 'EOM').
  • axis ({0 or ‘index’, 1 or ‘columns’, None}, default 0) Corresponds to the axis that contains the Index.

Notes

If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown

method

truncate(before=None, after=None, axis=None, copy=True)

Truncate a Series or DataFrame before and after some index value.

This is a useful shorthand for boolean indexing based on index values above or below certain thresholds.

Parameters
  • before (date, str, int) Truncate all rows before this index value.
  • after (date, str, int) Truncate all rows after this index value.
  • axis ({0 or 'index', 1 or 'columns'}, optional) Axis to truncate. Truncates the index (rows) by default.
  • copy (bool, default is True,) Return a copy of the truncated section.
Returns (type of caller)

The truncated Series or DataFrame.

See Also

DataFrame.loc : Select a subset of a DataFrame by label.DataFrame.iloc : Select a subset of a DataFrame by position.

Notes

If the index being truncated contains only datetime values, before and after may be specified as strings instead of Timestamps.

Examples
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],...                    'B': ['f', 'g', 'h', 'i', 'j'],
...                    'C': ['k', 'l', 'm', 'n', 'o']},
...                   index=[1, 2, 3, 4, 5])
>>> df
   A  B  C
1  a  f  k
2  b  g  l
3  c  h  m
4  d  i  n
5  e  j  o
>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n

The columns of a DataFrame can be truncated.

>>> df.truncate(before="A", after="B", axis="columns")
   A  B
1  a  f
2  b  g
3  c  h
4  d  i
5  e  j

For Series, only rows can be truncated.

>>> df['A'].truncate(before=2, after=4)
2    b
3    c
4    d
Name: A, dtype: object

The index values in truncate can be datetimes or string dates.

>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
                     A
2016-01-31 23:59:56  1
2016-01-31 23:59:57  1
2016-01-31 23:59:58  1
2016-01-31 23:59:59  1
2016-02-01 00:00:00  1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
...             after=pd.Timestamp('2016-01-10')).tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Because the index is a DatetimeIndex containing only dates, we can specify before and after as strings. They will be coerced to Timestamps before truncation.

>>> df.truncate('2016-01-05', '2016-01-10').tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1

Note that truncate assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates.

>>> df.loc['2016-01-05':'2016-01-10', :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1
method

tz_convert(tz, axis=0, level=None, copy=True)

Convert tz-aware axis to target time zone.

Parameters
  • level (int, str, default None) If axis is a MultiIndex, convert a specific level. Otherwisemust be None.
  • copy (bool, default True) Also make a copy of the underlying data.
Returns ({klass})

Object with time zone converted axis.

Raises
  • TypeError If the axis is tz-naive.
method

tz_localize(tz, axis=0, level=None, copy=True, ambiguous='raise', nonexistent='raise')

Localize tz-naive index of a Series or DataFrame to target time zone.

This operation localizes the Index. To localize the values in a timezone-naive Series, use :meth:Series.dt.tz_localize.

Parameters
  • level (int, str, default None) If axis ia a MultiIndex, localize a specific level. Otherwisemust be None.
  • copy (bool, default True) Also make a copy of the underlying data.
  • ambiguous ('infer', bool-ndarray, 'NaT', default 'raise') When clocks moved backward due to DST, ambiguous times may arise.For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the ambiguous parameter dictates how ambiguous times should be handled.
    • 'infer' will attempt to infer fall dst-transition hours based on order
    • bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
    • 'NaT' will return NaT where there are ambiguous times
    • 'raise' will raise an AmbiguousTimeError if there are ambiguous times.
  • nonexistent (str, default 'raise') A nonexistent time does not exist in a particular timezonewhere clocks moved forward due to DST. Valid values are:
    • 'shift_forward' will shift the nonexistent time forward to the closest existing time
    • 'shift_backward' will shift the nonexistent time backward to the closest existing time
    • 'NaT' will return NaT where there are nonexistent times
    • timedelta objects will shift nonexistent times by the timedelta
    • 'raise' will raise an NonExistentTimeError if there are nonexistent times.
Returns (Series or DataFrame)

Same type as the input.

Raises
  • TypeError If the TimeSeries is tz-aware and tz is not None.
Examples

Localize local times:

>>> s = pd.Series([1],
...               index=pd.DatetimeIndex(['2018-09-15 01:30:00']))
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00    1
dtype: int64

Be careful with DST changes. When there is sequential data, pandas can infer the DST time:

>>> s = pd.Series(range(7),
...               index=pd.DatetimeIndex(['2018-10-28 01:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 02:00:00',
...                                       '2018-10-28 02:30:00',
...                                       '2018-10-28 03:00:00',
...                                       '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00    0
2018-10-28 02:00:00+02:00    1
2018-10-28 02:30:00+02:00    2
2018-10-28 02:00:00+01:00    3
2018-10-28 02:30:00+01:00    4
2018-10-28 03:00:00+01:00    5
2018-10-28 03:30:00+01:00    6
dtype: int64

In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly

>>> s = pd.Series(range(3),
...               index=pd.DatetimeIndex(['2018-10-28 01:20:00',
...                                       '2018-10-28 02:36:00',
...                                       '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00    0
2018-10-28 02:36:00+02:00    1
2018-10-28 03:46:00+01:00    2
dtype: int64

If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or 'shift_forward' or 'shift_backward'.

>>> s = pd.Series(range(2),
...               index=pd.DatetimeIndex(['2015-03-29 02:30:00',
...                                       '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00    0
2015-03-29 03:30:00+02:00              1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))
2015-03-29 03:30:00+02:00    0
2015-03-29 03:30:00+02:00    1
dtype: int64
method

abs()

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns (abs)

Series/DataFrame containing the absolute value of each element.

See Also

numpy.absolute : Calculate the absolute value element-wise.

Notes

For complex inputs, 1.2 + 1j, the absolute value is :math:\sqrt{ a^2 + b^2 }.

Examples

Absolute numeric values in a Series.

>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0    1.10
1    2.00
2    3.33
3    4.00
dtype: float64

Absolute numeric values in a Series with complex numbers.

>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0    1.56205
dtype: float64

Absolute numeric values in a Series with a Timedelta element.

>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0   1 days
dtype: timedelta64[ns]

Select rows with data closest to certain value using argsort (from StackOverflow <https://stackoverflow.com/a/17758115>__).

>>> df = pd.DataFrame({
...     'a': [4, 5, 6, 7],
...     'b': [10, 20, 30, 40],
...     'c': [100, 50, -30, -50]
... })
>>> df
     a    b    c
0    4   10  100
1    5   20   50
2    6   30  -30
3    7   40  -50
>>> df.loc[(df.c - 43).abs().argsort()]
     a    b    c
1    5   20   50
0    4   10  100
2    6   30  -30
3    7   40  -50
method

describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

Parameters
  • percentiles (list-like of numbers, optional) The percentiles to include in the output. All shouldfall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
  • include ('all', list-like of dtypes or None (default), optional) A white list of data types to include in the result. Ignoredfor Series. Here are the options:
    • 'all' : All columns of the input will be included in the output.
    • A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
    • None (default) : The result will include all numeric columns.
  • exclude (list-like of dtypes or None (default), optional,) A black list of data types to omit from the result. Ignoredfor Series. Here are the options:
    • A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To exclude pandas categorical columns, use 'category'
    • None (default) : The result will exclude nothing.
  • datetime_is_numeric (bool, default False) Whether to treat datetime dtypes as numeric. This affects statisticscalculated for the column. For DataFrame input, this also controls whether datetime columns are included by default.
    .. versionadded:: 1.1.0
Returns (Series or DataFrame)

Summary statistics of the Series or Dataframe provided.

See Also

DataFrame.count: Count number of non-NA/null observations.DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype.

Notes

For numeric data, the result's index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result's index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value's frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Examples

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
dtype: float64

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01")
... ])
>>> s.describe(datetime_is_numeric=True)
count                      3
mean     2006-09-01 08:00:00
min      2000-01-01 00:00:00
25%      2004-12-31 12:00:00
50%      2010-01-01 00:00:00
75%      2010-01-01 00:00:00
max      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
...                    'numeric': [1, 2, 3],
...                    'object': ['a', 'b', 'c']
...                   })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')  # doctest: +SKIP
       categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      a
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[object])  # doctest: +SKIP
       object
count       3
unique      3
top         a
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])
       categorical
count            3
unique           3
top              d
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])  # doctest: +SKIP
       categorical object
count            3      3
unique           3      3
top              f      a
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[object])  # doctest: +SKIP
       categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0
method

pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)

Percentage change between the current and a prior element.

Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements.

Parameters
  • periods (int, default 1) Periods to shift for forming percent change.
  • fill_method (str, default 'pad') How to handle NAs before computing percent changes.
  • limit (int, default None) The number of consecutive NAs to fill before stopping.
  • freq (DateOffset, timedelta, or str, optional) Increment to use from time series API (e.g. 'M' or BDay()).
  • **kwargs Additional keyword arguments are passed intoDataFrame.shift or Series.shift.
Returns (chg : Series or DataFrame)

The same type as the calling object.

See Also

Series.diff : Compute the difference of two elements in a Series.DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods.

Examples

Series

>>> s = pd.Series([90, 91, 85])
>>> s
0    90
1    91
2    85
dtype: int64
>>> s.pct_change()
0         NaN
1    0.011111
2   -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0         NaN
1         NaN
2   -0.055556
dtype: float64

See the percentage change in a Series where filling NAs with last valid observation forward to next valid.

>>> s = pd.Series([90, 91, None, 85])
>>> s
0    90.0
1    91.0
2     NaN
3    85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0         NaN
1    0.011111
2    0.000000
3   -0.065934
dtype: float64

DataFrame

Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01.

>>> df = pd.DataFrame({
...     'FR': [4.0405, 4.0963, 4.3149],
...     'GR': [1.7246, 1.7482, 1.8519],
...     'IT': [804.74, 810.01, 860.13]},
...     index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
                FR      GR      IT
1980-01-01  4.0405  1.7246  804.74
1980-02-01  4.0963  1.7482  810.01
1980-03-01  4.3149  1.8519  860.13
>>> df.pct_change()
                  FR        GR        IT
1980-01-01       NaN       NaN       NaN
1980-02-01  0.013810  0.013684  0.006549
1980-03-01  0.053365  0.059318  0.061876

Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns.

>>> df = pd.DataFrame({
...     '2016': [1769950, 30586265],
...     '2015': [1500923, 40912316],
...     '2014': [1371819, 41403351]},
...     index=['GOOG', 'APPL'])
>>> df
          2016      2015      2014
GOOG   1769950   1500923   1371819
APPL  30586265  40912316  41403351
>>> df.pct_change(axis='columns', periods=-1)
          2016      2015  2014
GOOG  0.179241  0.094112   NaN
APPL -0.252395 -0.011860   NaN
method

any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) Indicate which axis or axes should be reduced.
    • 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
    • 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
    • None : reduce all axes, return a scalar.
  • bool_only (bool, default None) Include only boolean columns. If None, will attempt to use everything,then use only boolean data. Not implemented for Series.
  • skipna (bool, default True) Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • **kwargs (any, default None) Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.

See Also

numpy.any : Numpy version of this method.Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis.

Examples

Series

For Series input, the output is a scalar indicating whether any element is True.

>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([], dtype="float64").any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True

DataFrame

Whether each column contains at least one True element (the default).

>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
   A  B  C
0  1  0  0
1  2  2  0
>>> df.any()
A     True
B     True
C    False
dtype: bool

Aggregating over the columns.

>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
       A  B
0   True  1
1  False  2
>>> df.any(axis='columns')
0    True
1    True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
       A  B
0   True  1
1  False  0
>>> df.any(axis='columns')
0    True
1    False
dtype: bool

Aggregating over the entire DataFrame with axis=None.

>>> df.any(axis=None)
True

any for an empty DataFrame is an empty Series.

>>> pd.DataFrame([]).any()
Series([], dtype: bool)
method

all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).

Parameters
  • axis ({0 or 'index', 1 or 'columns', None}, default 0) Indicate which axis or axes should be reduced.
    • 0 / 'index' : reduce the index, return a Series whose index is the original column labels.
    • 1 / 'columns' : reduce the columns, return a Series whose index is the original index.
    • None : reduce all axes, return a scalar.
  • bool_only (bool, default None) Include only boolean columns. If None, will attempt to use everything,then use only boolean data. Not implemented for Series.
  • skipna (bool, default True) Exclude NA/null values. If the entire row/column is NA and skipna isTrue, then the result will be True, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • **kwargs (any, default None) Additional keywords have no effect but might be accepted forcompatibility with NumPy.
Returns (Series or DataFrame)

If level is specified, then, DataFrame is returned; otherwise, Seriesis returned.

See Also

Series.all : Return True if all elements are True.DataFrame.any : Return True if one (or more) elements are True.

Examples

Series

>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([], dtype="float64").all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True

DataFrames

Create a dataframe from a dictionary.

>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
   col1   col2
0  True   True
1  True  False

Default behaviour checks if column-wise values all return True.

>>> df.all()
col1     True
col2    False
dtype: bool

Specify axis='columns' to check if row-wise values all return True.

>>> df.all(axis='columns')
0     True
1    False
dtype: bool

Or axis=None for whether every value is True.

>>> df.all(axis=None)
False
method

mad(axis=None, skipna=None, level=None)

Return the mean absolute deviation of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default None) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
method

sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • ddof (int, default 1) Delta Degrees of Freedom. The divisor used in calculations is N - ddof,where N represents the number of elements.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.

Notes

To have the same behaviour as numpy.std, use ddof=0 (instead of the default ddof=1)

method

cummin(axis=None, skipna=True, *args, **kwargs)

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative minimum of Series or DataFrame.

See Also

core.window.Expanding.min : Similar functionality but ignores NaN values. DataFrame.min : Return the minimum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummin()
0    2.0
1    NaN
2    2.0
3   -1.0
4   -1.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummin(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the minimum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummin()
     A    B
0  2.0  1.0
1  2.0  NaN
2  1.0  0.0

To iterate over columns and find the minimum in each row, use axis=1

>>> df.cummin(axis=1)
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0
method

cummax(axis=None, skipna=True, *args, **kwargs)

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative maximum of Series or DataFrame.

See Also

core.window.Expanding.max : Similar functionality but ignores NaN values. DataFrame.max : Return the maximum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cummax()
0    2.0
1    NaN
2    5.0
3    5.0
4    5.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cummax(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the maximum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cummax()
     A    B
0  2.0  1.0
1  3.0  NaN
2  3.0  1.0

To iterate over columns and find the maximum in each row, use axis=1

>>> df.cummax(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  1.0
method

cumsum(axis=None, skipna=True, *args, **kwargs)

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative sum of Series or DataFrame.

See Also

core.window.Expanding.sum : Similar functionality but ignores NaN values. DataFrame.sum : Return the sum over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumsum()
0    2.0
1    NaN
2    7.0
3    6.0
4    6.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumsum(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

To iterate over columns and find the sum in each row, use axis=1

>>> df.cumsum(axis=1)
     A    B
0  2.0  3.0
1  3.0  NaN
2  1.0  1.0
method

cumprod(axis=None, skipna=True, *args, **kwargs)

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The index or the name of the axis. 0 is equivalent to None or 'index'.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series or DataFrame)

Return cumulative product of Series or DataFrame.

See Also

core.window.Expanding.prod : Similar functionality but ignores NaN values. DataFrame.prod : Return the product over DataFrame axis. DataFrame.cummax : Return cumulative maximum over DataFrame axis. DataFrame.cummin : Return cumulative minimum over DataFrame axis. DataFrame.cumsum : Return cumulative sum over DataFrame axis. DataFrame.cumprod : Return cumulative product over DataFrame axis.

Examples

Series

>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0    2.0
1    NaN
2    5.0
3   -1.0
4    0.0
dtype: float64

By default, NA values are ignored.

>>> s.cumprod()
0     2.0
1     NaN
2    10.0
3   -10.0
4    -0.0
dtype: float64

To include NA values in the operation, use skipna=False

>>> s.cumprod(skipna=False)
0    2.0
1    NaN
2    NaN
3    NaN
4    NaN
dtype: float64

DataFrame

>>> df = pd.DataFrame([[2.0, 1.0],
...                    [3.0, np.nan],
...                    [1.0, 0.0]],
...                    columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the product in each column. This is equivalent to axis=None or axis='index'.

>>> df.cumprod()
     A    B
0  2.0  1.0
1  6.0  NaN
2  6.0  0.0

To iterate over columns and find the product in each row, use axis=1

>>> df.cumprod(axis=1)
     A    B
0  2.0  2.0
1  3.0  NaN
2  1.0  0.0
method

sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • min_count (int, default 0) The required number of valid values to perform the operation. If fewer thanmin_count non-NA values are present the result will be NA.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.sum()
14

By default, the sum of an empty or all-NA Series is 0.

>>> pd.Series([], dtype="float64").sum()  # min_count=0 is the default
0.0

This can be controlled with the min_count parameter. For example, if you'd like the sum of an empty series to be NaN, pass min_count=1.

>>> pd.Series([], dtype="float64").sum(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nan
method

prod(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Return the product of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • min_count (int, default 0) The required number of valid values to perform the operation. If fewer thanmin_count non-NA values are present the result will be NA.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples

By default, the product of an empty or all-NA Series is 1

>>> pd.Series([], dtype="float64").prod()
1.0

This can be controlled with the min_count parameter

>>> pd.Series([], dtype="float64").prod(min_count=1)
nan

Thanks to the skipna parameter, min_count handles all-NA and empty series identically.

>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan
method

mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the mean of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased skew over requested axis.

Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the median of the values over the requested axis.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
method

max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the maximum of the values over the requested axis.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.max()
8
method

min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Parameters
  • axis ({index (0), columns (1)}) Axis for the function to be applied on.
  • skipna (bool, default True) Exclude NA/null values when computing the result.
  • level (int or level name, default None) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a Series.
  • numeric_only (bool, default None) Include only float, int, boolean columns. If None, will attempt to useeverything, then use only numeric data. Not implemented for Series.
  • **kwargs Additional keyword arguments to be passed to the function.
See Also

Series.sum : Return the sum.Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.

Examples
>>> idx = pd.MultiIndex.from_arrays([...     ['warm', 'warm', 'cold', 'cold'],
...     ['dog', 'falcon', 'fish', 'spider']],
...     names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded  animal
warm     dog       4
         falcon    2
cold     fish      0
         spider    8
Name: legs, dtype: int64
>>> s.min()
0
method

rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, method='single')

Provide rolling window calculations.

Parameters
  • window (int, offset, or BaseIndexer subclass) Size of the moving window. This is the number of observations used forcalculating the statistic. Each window will be a fixed size.
    If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes.
    If a BaseIndexer subclass is passed, calculates the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, and closed will be passed to get_window_bounds.
  • min_periods (int, default None) Minimum number of observations in window required to have a value(otherwise result is NA). For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window.
  • center (bool, default False) Set the labels at the center of the window.
  • win_type (str, default None) Provide a window type. If None, all points are evenly weighted.See the notes below for further information.
  • on (str, optional) For a DataFrame, a datetime-like column or Index level on whichto calculate the rolling window, rather than the DataFrame's index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.
  • closed (str, default None) Make the interval closed on the 'right', 'left', 'both' or'neither' endpoints. Defaults to 'right'.
    .. versionchanged:: 1.2.0
    The closed parameter with fixed windows is now supported.
    
  • method (str {'single', 'table'}, default 'single') Execute the rolling operation per single column or row ('single')or over the entire object ('table').
    This argument is only implemented when specifying engine='numba' in the method call.
    .. versionadded:: 1.3.0
See Also

expanding : Provides expanding transformations.ewm : Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

To learn more about the offsets & frequency strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.

If win_type=None, all points are evenly weighted; otherwise, win_type can accept a string of any scipy.signal window function <https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows>__.

Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature. Please see the third example below on how to add the additional parameters.

Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

Rolling sum with a window length of 2, using the 'triang' window type.

>>> df.rolling(2, win_type='triang').sum()
     B
0  NaN
1  0.5
2  1.5
3  NaN
4  NaN

Rolling sum with a window length of 2, using the 'gaussian' window type (note how we need to specify std).

>>> df.rolling(2, win_type='gaussian').sum(std=3)
          B
0       NaN
1  0.986207
2  2.958621
3       NaN
4       NaN

Rolling sum with a window length of 2, min_periods defaults to the window length.

>>> df.rolling(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  NaN
4  NaN

Same as above, but explicitly set the min_periods

>>> df.rolling(2, min_periods=1).sum()
     B
0  0.0
1  1.0
2  3.0
3  2.0
4  4.0

Same as above, but with forward-looking windows

>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
>>> df.rolling(window=indexer, min_periods=1).sum()
     B
0  1.0
1  3.0
2  2.0
3  4.0
4  4.0

A ragged (meaning not-a-regular frequency), time-indexed DataFrame

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
...                   index = [pd.Timestamp('20130101 09:00:00'),
...                            pd.Timestamp('20130101 09:00:02'),
...                            pd.Timestamp('20130101 09:00:03'),
...                            pd.Timestamp('20130101 09:00:05'),
...                            pd.Timestamp('20130101 09:00:06')])
>>> df
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. The default for min_periods is 1.

>>> df.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0
method

expanding(min_periods=1, center=None, axis=0, method='single')

Provide expanding transformations.

Parameters
  • min_periods (int, default 1) Minimum number of observations in window required to have a value(otherwise result is NA).
  • center (bool, default False) Set the labels at the center of the window.
  • method (str {'single', 'table'}, default 'single') Execute the rolling operation per single column or row ('single')or over the entire object ('table').
    This argument is only implemented when specifying engine='numba' in the method call.
    .. versionadded:: 1.3.0
See Also

rolling : Provides rolling window calculations.ewm : Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

Examples
>>> df = pd.DataFrame({"B": [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.expanding(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  3.0
4  7.0
method

ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, times=None)

Provide exponential weighted (EW) functions.

Available EW functions: mean(), var(), std(), corr(), cov().

Exactly one parameter: com, span, halflife, or alpha must be provided.

Parameters
  • com (float, optional) Specify decay in terms of center of mass,:math:\alpha = 1 / (1 + com), for :math:com \geq 0.
  • span (float, optional) Specify decay in terms of span,:math:\alpha = 2 / (span + 1), for :math:span \geq 1.
  • halflife (float, str, timedelta, optional) Specify decay in terms of half-life,:math:\alpha = 1 - \exp\left(-\ln(2) / halflife\right), for :math:halflife > 0.
    If times is specified, the time unit (str or timedelta) over which an observation decays to half its value. Only applicable to mean() and halflife value will not apply to the other functions.
    .. versionadded:: 1.1.0
  • alpha (float, optional) Specify smoothing factor :math:\alpha directly,:math:0 < \alpha \leq 1.
  • min_periods (int, default 0) Minimum number of observations in window required to have a value(otherwise result is NA).
  • adjust (bool, default True) Divide by decaying adjustment factor in beginning periods to accountfor imbalance in relative weightings (viewing EWMA as a moving average).
    • When adjust=True (default), the EW function is calculated using weights :math:w_i = (1 - \alpha)^i. For example, the EW moving average of the series [:math:x_0, x_1, ..., x_t] would be:
    .. math:: y_t = \frac{x_t + (1 - \alpha)x_{t-1} + (1 - \alpha)^2 x_{t-2} + ... + (1 - \alpha)^t x_0}{1 + (1 - \alpha) + (1 - \alpha)^2 + ... + (1 - \alpha)^t}
    • When adjust=False, the exponentially weighted function is calculated recursively:
    .. math:: \begin{split} y_0 &= x_0\ y_t &= (1 - \alpha) y_{t-1} + \alpha x_t, \end{split}
  • ignore_na (bool, default False) Ignore missing values when calculating weights; specify True to reproducepre-0.15.0 behavior.
    • When ignore_na=False (default), weights are based on absolute positions. For example, the weights of :math:x_0 and :math:x_2 used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:(1-\alpha)^2 and :math:1 if adjust=True, and :math:(1-\alpha)^2 and :math:\alpha if adjust=False.
    • When ignore_na=True (reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of :math:x_0 and :math:x_2 used in calculating the final weighted average of [:math:x_0, None, :math:x_2] are :math:1-\alpha and :math:1 if adjust=True, and :math:1-\alpha and :math:\alpha if adjust=False.
  • axis ({0, 1}, default 0) The axis to use. The value 0 identifies the rows, and 1identifies the columns.
  • times (str, np.ndarray, Series, default None) 0
    d .
    .
    .
    .
Returns (DataFrame)

A Window sub-classed for the particular operation.

See Also

rolling : Provides rolling window calculations.expanding : Provides expanding transformations.

Notes

: .

Examples
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.ewm(com=0.5).mean()
          B
0  0.000000
1  0.750000
2  1.615385
3  1.615385
4  3.670213

Specifying times with a timedelta halflife when computing mean.

>>> times = ['2020-01-01', '2020-01-03', '2020-01-10', '2020-01-15', '2020-01-17']
>>> df.ewm(halflife='4 days', times=pd.DatetimeIndex(times)).mean()
          B
0  0.000000
1  0.585786
2  1.523889
3  1.523889
4  3.233686
method

first_valid_index()

Return index for first non-NA value or None, if no NA value is found.

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

method

last_valid_index()

Return index for last non-NA value or None, if no NA value is found.

Notes

If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.

method

__repr__() → str

Return a string representation for a particular DataFrame.

method

to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, min_rows=None, max_cols=None, show_dimensions=False, decimal='.', line_width=None, max_colwidth=None, encoding=None)

Render a DataFrame to a console-friendly tabular output.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (sequence, optional, default None) The subset of columns to write. Writes all columns by default.
  • col_space (int, list or dict of int, optional) The minimum width of each column.
  • header (bool or sequence, optional) Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
  • index (bool, optional, default True) Whether to print index (row) labels.
  • na_rep (str, optional, default 'NaN') String representation of NaN to use.
  • formatters (list, tuple or dict of one-param. functions, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
  • float_format (one-parameter function, optional, default None) Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
    .. versionchanged:: 1.2.0
  • sparsify (bool, optional, default True) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.
  • index_names (bool, optional, default True) Prints the names of the indexes.
  • justify (str, default None) How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
    • left
    • right
    • center
    • justify
    • justify-all
    • start
    • end
    • inherit
    • match-parent
    • initial
    • unset.
  • max_rows (int, optional) Maximum number of rows to display in the console.
  • min_rows (int, optional) The number of rows to display in the console in a truncated repr(when number of rows is above max_rows).
  • max_cols (int, optional) Maximum number of columns to display in the console.
  • show_dimensions (bool, default False) Display DataFrame dimensions (number of rows by number of columns).
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • line_width (int, optional) Width to wrap a line in characters.
  • max_colwidth (int, optional) Max width to truncate each column in characters. By default, no limit.
    .. versionadded:: 1.0.0
  • encoding (str, default "utf-8") Set character encoding.
    .. versionadded:: 1.0
Returns (str or None)

If buf is None, returns the result as a string. Otherwise returnsNone.

See Also

to_html : Convert DataFrame to HTML.

Examples
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}>>> df = pd.DataFrame(d)
>>> print(df.to_string())
   col1  col2
0     1     4
1     2     5
2     3     6
generator

items()

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields (label : object)

The column names for the DataFrame being iterated over.ent : Series The column entries belonging to each label, as a Series.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.

Examples
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
generator

iteritems()

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Yields (label : object)

The column names for the DataFrame being iterated over.ent : Series The column entries belonging to each label, as a Series.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.

Examples
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],...                   'population': [1864, 22000, 80000]},
...                   index=['panda', 'polar', 'koala'])
>>> df
        species   population
panda   bear      1864
polar   bear      22000
koala   marsupial 80000
>>> for label, content in df.items():
...     print(f'label: {label}')
...     print(f'content: {content}', sep='\n')
...
label: species
content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: object
label: population
content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: int64
generator

iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

Yields (index : label or tuple of label)

The index of the row. A tuple for a MultiIndex. : Series The data of the row as a Series.

See Also

DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.DataFrame.items : Iterate over (column name, Series) pairs.

Notes

  1. Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int      1.0
float    1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64

To preserve dtypes while iterating over the rows, it is better to use :meth:itertuples which returns namedtuples of the values and which is generally faster than iterrows.

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

method

itertuples(index=True, name='Pandas')

Iterate over DataFrame rows as namedtuples.

Parameters
  • index (bool, default True) If True, return the index as the first element of the tuple.
  • name (str or None, default "Pandas") The name of the returned namedtuples or None to return regulartuples.
Returns (iterator)

An object to iterate over namedtuples for each row in theDataFrame with the first field possibly being the index and following fields being the column values.

See Also

DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs. DataFrame.items : Iterate over (column name, Series) pairs.

Notes

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. On python versions < 3.7 regular tuples are returned for DataFrames with a large number of columns (>254).

Examples
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},...                   index=['dog', 'hawk'])
>>> df
      num_legs  num_wings
dog          4          0
hawk         2          2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)

By setting the index parameter to False we can remove the index as the first element of the tuple:

>>> for row in df.itertuples(index=False):
...     print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)

With the name parameter set we set a custom name for the yielded namedtuples:

>>> for row in df.itertuples(name='Animal'):
...     print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
method

__len__() → int

Returns length of info axis, but here we use the index.

method

dot(other)

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array.

It can also be called using self @ other in Python >= 3.5.

Parameters
  • other (Series, DataFrame or array-like) The other object to compute the matrix product with.
Returns (Series or DataFrame)

If other is a Series, return the matrix product between self andother as a Series. If other is a DataFrame or a numpy.array, return the matrix product of self and other in a DataFrame of a np.array.

See Also

Series.dot: Similar method for Series.

Notes

The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples

Here we multiply a DataFrame with a Series.

>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0    -4
1     5
dtype: int64

Here we multiply a DataFrame with another DataFrame.

>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
    0   1
0   1   4
1   2   2

Note that the dot method give the same result as @

>>> df @ other
    0   1
0   1   4
1   2   2

The dot method works also if other is an np.array.

>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
    0   1
0   1   4
1   2   2

Note how shuffling of the objects does not change the result.

>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0    -4
1     5
dtype: int64
method

__matmul__(other) → DataFrame or Series

Matrix multiplication using binary @ operator in Python>=3.5.

method

__rmatmul__(other)

Matrix multiplication using binary @ operator in Python>=3.5.

classmethod

from_dict(data, orient='columns', dtype=None, columns=None)

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters
  • data (dict) Of the form {field : array-like} or {field : dict}.
  • orient ({'columns', 'index'}, default 'columns') The "orientation" of the data. If the keys of the passed dictshould be the columns of the resulting DataFrame, pass 'columns' (default). Otherwise if the keys should be rows, pass 'index'.
  • dtype (dtype, default None) Data type to force, otherwise infer.
  • columns (list, default None) Column labels to use when orient='index'. Raises a ValueErrorif used with orient='columns'.
See Also

DataFrame.from_records : DataFrame from structured ndarray, sequence of tuples or dicts, or DataFrame. DataFrame : DataFrame object creation using constructor.

Examples

By default the keys of the dict become the DataFrame columns:

>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Specify orient='index' to create the DataFrame using dictionary keys as rows:

>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
       0  1  2  3
row_1  3  2  1  0
row_2  a  b  c  d

When using the 'index' orientation, the column names can be specified manually:

>>> pd.DataFrame.from_dict(data, orient='index',
...                        columns=['A', 'B', 'C', 'D'])
       A  B  C  D
row_1  3  2  1  0
row_2  a  b  c  d
method

to_numpy(dtype=None, copy=False, na_value=<no_default>)

Convert the DataFrame to a NumPy array.

By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are float16 and float32, the results dtype will be float32. This may require copying data and coercing values, which may be expensive.

Parameters
  • dtype (str or numpy.dtype, optional) The dtype to pass to :meth:numpy.asarray.
  • copy (bool, default False) Whether to ensure that the returned value is not a view onanother array. Note that copy=False does not ensure that to_numpy() is no-copy. Rather, copy=True ensure that a copy is made, even if not strictly necessary.
  • na_value (Any, optional) The value to use for missing values. The default value dependson dtype and the dtypes of the DataFrame columns.
    .. versionadded:: 1.1.0
See Also

Series.to_numpy : Similar method for Series.

Examples
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()array([[1, 3],
       [2, 4]])

With heterogeneous data, the lowest common type will have to be used.

>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
       [2. , 4.5]])

For a mix of numeric and non-numeric types, the output array will have object dtype.

>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
       [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
method

to_dict(orient='dict', into=<class 'dict'>)

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Parameters
  • orient (str {'dict', 'list', 'series', 'split', 'records', 'index'}) Determines the type of the values of the dictionary.
    • 'dict' (default) : dict like {column -> {index -> value}}
    • 'list' : dict like {column -> [values]}
    • 'series' : dict like {column -> Series(values)}
    • 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
    • 'records' : list like [{column -> value}, ... , {column -> value}]
    • 'index' : dict like {index -> {column -> value}}
    Abbreviations are allowed. s indicates series and sp indicates split.
  • into (class, default dict) The collections.abc.Mapping subclass used for all Mappingsin the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.
Returns (dict, list or collections.abc.Mapping)

Return a collections.abc.Mapping object representing the DataFrame.The resulting transformation depends on the orient parameter.

See Also

DataFrame.from_dict: Create a DataFrame from a dictionary.DataFrame.to_json: Convert a DataFrame to JSON format.

Examples
>>> df = pd.DataFrame({'col1': [1, 2],...                    'col2': [0.5, 0.75]},
...                   index=['row1', 'row2'])
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
method

to_gbq(destination_table, project_id=None, chunksize=None, reauth=False, if_exists='fail', auth_local_webserver=False, table_schema=None, location=None, progress_bar=True, credentials=None)

Write a DataFrame to a Google BigQuery table.

This function requires the pandas-gbq package <https://pandas-gbq.readthedocs.io>__.

See the How to authenticate with Google BigQuery <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>__ guide for authentication instructions.

Parameters
  • destination_table (str) Name of table to be written, in the form dataset.tablename.
  • project_id (str, optional) Google BigQuery Account project ID. Optional when available fromthe environment.
  • chunksize (int, optional) Number of rows to be inserted in each chunk from the dataframe.Set to None to load the whole dataframe at once.
  • reauth (bool, default False) Force Google BigQuery to re-authenticate the user. This is usefulif multiple accounts are used.
  • if_exists (str, default 'fail') Behavior when the destination table exists. Value can be one of:
    'fail' If table exists raise pandasgbq.gbq.TableCreationError. 'replace' If table exists, drop it, recreate it, and insert data. 'append' If table exists, insert data. Create if does not exist.
  • auth_local_webserver (bool, default False) Use the local webserver flow instead of the console flowwhen getting user credentials.
    .. _local webserver flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server .. _console flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
    New in version 0.2.0 of pandas-gbq.
  • table_schema (list of dicts, optional) List of BigQuery table fields to which according DataFramecolumns conform to, e.g. [{'name': 'col1', 'type': 'STRING'},...]. If schema is not provided, it will be generated according to dtypes of DataFrame columns. See BigQuery API documentation on available names of a field.
    New in version 0.3.1 of pandas-gbq.
  • location (str, optional) Location where the load job should run. See the BigQuery locationsdocumentation <https://cloud.google.com/bigquery/docs/dataset-locations>__ for a list of available locations. The location must match that of the target dataset.
    New in version 0.5.0 of pandas-gbq.
  • progress_bar (bool, default True) Use the library tqdm to show the progress bar for the upload,chunk by chunk.
    New in version 0.5.0 of pandas-gbq.
  • credentials (google.auth.credentials.Credentials, optional) Credentials for accessing Google APIs. Use this parameter tooverride default credentials, such as to use Compute Engine :class:google.auth.compute_engine.Credentials or Service Account :class:google.oauth2.service_account.Credentials directly.
    New in version 0.8.0 of pandas-gbq.
See Also

pandas_gbq.to_gbq : This function in the pandas-gbq library.read_gbq : Read a DataFrame from Google BigQuery.

classmethod

from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)

Convert structured or record ndarray to DataFrame.

Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame.

Parameters
  • data (structured ndarray, sequence of tuples or dicts, or DataFrame) Structured input data.
  • index (str, list of fields, array-like) Field of array to use as the index, alternately a specific set ofinput labels to use.
  • exclude (sequence, default None) Columns or fields to exclude.
  • columns (sequence, default None) Column names to use. If the passed data do not have namesassociated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).
  • coerce_float (bool, default False) Attempt to convert values of non-string, non-numeric objects (likedecimal.Decimal) to floating point, useful for SQL result sets.
  • nrows (int, default None) Number of rows to read if data is an iterator.
See Also

DataFrame.from_dict : DataFrame from dict of array-like or dicts.DataFrame : DataFrame object creation using constructor.

Examples

Data can be provided as a structured ndarray:

>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
...                 dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of dicts:

>>> data = [{'col_1': 3, 'col_2': 'a'},
...         {'col_1': 2, 'col_2': 'b'},
...         {'col_1': 1, 'col_2': 'c'},
...         {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d

Data can be provided as a list of tuples with corresponding columns:

>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
   col_1 col_2
0      3     a
1      2     b
2      1     c
3      0     d
method

to_records(index=True, column_dtypes=None, index_dtypes=None)

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Parameters
  • index (bool, default True) Include index in resulting record array, stored in 'index'field or using the index label, if set.
  • column_dtypes (str, type, dict, default None) If a string or type, the data type to store all columns. Ifa dictionary, a mapping of column names and indices (zero-indexed) to specific data types.
  • index_dtypes (str, type, dict, default None) If a string or type, the data type to store all index levels. Ifa dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.
    This mapping is applied only if index=True.
Returns (numpy.recarray)

NumPy ndarray with the DataFrame labels as fields and each rowof the DataFrame as entries.

See Also

DataFrame.from_records: Convert structured or record ndarray to DataFrame. numpy.recarray: An ndarray that allows field access using attributes, analogous to typed columns in a spreadsheet.

Examples
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},...                   index=['a', 'b'])
>>> df
   A     B
a  1  0.50
b  2  0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])

If the DataFrame index has no label then the recarray field name is set to 'index'. If the index has a label then this is used as the field name:

>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])

The index can be excluded from the record array:

>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
          dtype=[('A', '<i8'), ('B', '<f8')])

Data types can be specified for the columns:

>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
          dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])

As well as for the index:

>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
          dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
method

to_stata(path, convert_dates=None, write_index=True, byteorder=None, time_stamp=None, data_label=None, variable_labels=None, version=114, convert_strl=None, compression='infer', storage_options=None)

Export DataFrame object to Stata dta format.

Writes the DataFrame to a Stata dataset file. "dta" files contain a Stata dataset.

Parameters
  • path (str, buffer or path object) String, path object (pathlib.Path or py._path.local.LocalPath) orobject implementing a binary write() function. If using a buffer then the buffer will not be automatically closed after the file data has been written.
    .. versionchanged:: 1.0.0
    Previously this was "fname"
  • convert_dates (dict) Dictionary mapping columns containing datetime types to statainternal format to use when writing the dates. Options are 'tc', 'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to 'tc'. Raises NotImplementedError if a datetime column has timezone information.
  • write_index (bool) Write the index to Stata dataset.
  • byteorder (str) Can be ">", "<", "little", or "big". default is sys.byteorder.
  • time_stamp (datetime) A datetime to use as file creation date. Default is the currenttime.
  • data_label (str, optional) A label for the data set. Must be 80 characters or smaller.
  • variable_labels (dict) Dictionary containing columns as keys and variable labels asvalues. Each label must be 80 characters or smaller.
  • version ({{114, 117, 118, 119, None}}, default 114) Version to use in the output dta file. Set to None to let pandasdecide between 118 or 119 formats depending on the number of columns in the frame. Version 114 can be read by Stata 10 and later. Version 117 can be read by Stata 13 or later. Version 118 is supported in Stata 14 and later. Version 119 is supported in Stata 15 and later. Version 114 limits string variables to 244 characters or fewer while versions 117 and later allow strings with lengths up to 2,000,000 characters. Versions 118 and 119 support Unicode characters, and version 119 supports more than 32,767 variables.
    Version 119 should usually only be used when the number of variables exceeds the capacity of dta format 118. Exporting smaller datasets in format 119 may have unintended consequences, and, as of November 2020, Stata SE cannot read version 119 files.
    .. versionchanged:: 1.0.0
    Added support for formats 118 and 119.
    
  • convert_strl (list, optional) List of column names to convert to string columns to Stata StrLformat. Only available if version is 117. Storing strings in the StrL format can produce smaller dta files if strings have more than 8 characters and values are repeated.
  • compression (str or dict, default 'infer') For on-the-fly compression of the output dta. If string, specifiescompression mode. If dict, value at key 'method' specifies compression mode. Compression mode must be one of {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}. If compression mode is 'infer' and fname is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no compression). If dict and compression mode is one of {{'zip', 'gzip', 'bz2'}}, or inferred as one of the above, other entries passed as additional compression options.
    .. versionadded:: 1.1.0
Raises
  • NotImplementedError
    • If datetimes contain timezone information
    • Column dtype is not representable in Stata
  • ValueError
    • Columns listed in convert_dates are neither datetime64[ns] or datetime.datetime
    • Column listed in convert_dates is not in DataFrame
    • Categorical label contains more than 32,000 characters
See Also

read_stata : Import Stata data files.io.stata.StataWriter : Low-level writer for Stata data files. io.stata.StataWriter117 : Low-level writer for version 117 files.

Examples
>>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',...                               'parrot'],
...                    'speed': [350, 18, 361, 15]}})
>>> df.to_stata('animals.dta')  # doctest: +SKIP
method

to_feather(path, **kwargs)

Write a DataFrame to the binary Feather format.

Parameters
  • path (str or file-like object) If a string, it will be used as Root Directory path.
  • **kwargs Additional keywords passed to :func:pyarrow.feather.write_feather.Starting with pyarrow 0.17, this includes the compression, compression_level, chunksize and version keywords.
    .. versionadded:: 1.1.0
method

to_markdown(buf=None, mode='wt', index=True, storage_options=None, **kwargs)

Print DataFrame in Markdown-friendly format.

.. versionadded:: 1.0.0

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • mode (str, optional) Mode in which file is opened, "wt" by default.
  • index (bool, optional, default True) Add index (row) labels.
    .. versionadded:: 1.1.0
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
    .. versionadded:: 1.2.0
  • **kwargs These parameters will be passed to tabulate <https://pypi.org/project/tabulate>_.
Returns (str)

DataFrame in Markdown-friendly format.

Notes

Requires the tabulate <https://pypi.org/project/tabulate>_ package.

Examples
>>> s = pd.Series(["elk", "pig", "dog", "quetzal"], name="animal")>>> print(s.to_markdown())
|    | animal   |
|---:|:---------|
|  0 | elk      |
|  1 | pig      |
|  2 | dog      |
|  3 | quetzal  |

Output markdown with a tabulate option.

>>> print(s.to_markdown(tablefmt="grid"))
+----+----------+
|    | animal   |
+====+==========+
|  0 | elk      |
+----+----------+
|  1 | pig      |
+----+----------+
|  2 | dog      |
+----+----------+
|  3 | quetzal  |
+----+----------+
method

to_parquet(path=None, engine='auto', compression='snappy', index=None, partition_cols=None, storage_options=None, **kwargs)

Write a DataFrame to the binary parquet format.

This function writes the dataframe as a parquet file <https://parquet.apache.org/>_. You can choose different parquet backends, and have the option of compression. See :ref:the user guide <io.parquet> for more details.

Parameters
  • path (str or file-like object, default None) If a string, it will be used as Root Directory pathwhen writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function) or io.BytesIO. The engine fastparquet does not accept file-like objects. If path is None, a bytes object is returned.
    .. versionchanged:: 1.2.0
    Previously this was "fname"
  • engine ({{'auto', 'pyarrow', 'fastparquet'}}, default 'auto') Parquet library to use. If 'auto', then the optionio.parquet.engine is used. The default io.parquet.engine behavior is to try 'pyarrow', falling back to 'fastparquet' if 'pyarrow' is unavailable.
  • compression ({{'snappy', 'gzip', 'brotli', None}}, default 'snappy') Name of the compression to use. Use None for no compression.
  • index (bool, default None) If True, include the dataframe's index(es) in the file output.If False, they will not be written to the file. If None, similar to True the dataframe's index(es) will be saved. However, instead of being saved as values, the RangeIndex will be stored as a range in the metadata so it doesn't require much space and is faster. Other indexes will be included as columns in the file output.
  • partition_cols (list, optional, default None) Column names by which to partition the dataset.Columns are partitioned in the order they are given. Must be None if path is not a string.
  • **kwargs Additional arguments passed to the parquet library. See:ref:pandas io <io.parquet> for more details.
See Also

read_parquet : Read a parquet file.DataFrame.to_csv : Write a csv file. DataFrame.to_sql : Write to a sql table. DataFrame.to_hdf : Write to hdf.

Notes

This function requires either the fastparquet <https://pypi.org/project/fastparquet> or pyarrow <https://arrow.apache.org/docs/python/> library.

Examples
>>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})>>> df.to_parquet('df.parquet.gzip',
...               compression='gzip')  # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip')  # doctest: +SKIP
   col1  col2
0     1     3
1     2     4

If you want to get a buffer to the parquet content you can use a io.BytesIO object, as long as you don't use partition_cols, which creates multiple files.

>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()
method

to_html(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, max_rows=None, max_cols=None, show_dimensions=False, decimal='.', bold_rows=True, classes=None, escape=True, notebook=False, border=None, table_id=None, render_links=False, encoding=None)

Render a DataFrame as an HTML table.

Parameters
  • buf (str, Path or StringIO-like, optional, default None) Buffer to write to. If None, the output is returned as a string.
  • columns (sequence, optional, default None) The subset of columns to write. Writes all columns by default.
  • col_space (str or int, list or dict of int or str, optional) The minimum width of each column in CSS length units. An int is assumed to be px units.
    .. versionadded:: 0.25.0 Ability to use str.
  • header (bool, optional) Whether to print column labels, default True.
  • index (bool, optional, default True) Whether to print index (row) labels.
  • na_rep (str, optional, default 'NaN') String representation of NaN to use.
  • formatters (list, tuple or dict of one-param. functions, optional) Formatter functions to apply to columns' elements by position orname. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
  • float_format (one-parameter function, optional, default None) Formatter function to apply to columns' elements if they arefloats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.
    .. versionchanged:: 1.2.0
  • sparsify (bool, optional, default True) Set to False for a DataFrame with a hierarchical index to printevery multiindex key at each row.
  • index_names (bool, optional, default True) Prints the names of the indexes.
  • justify (str, default None) How to justify the column labels. If None uses the option fromthe print configuration (controlled by set_option), 'right' out of the box. Valid values are
    • left
    • right
    • center
    • justify
    • justify-all
    • start
    • end
    • inherit
    • match-parent
    • initial
    • unset.
  • max_rows (int, optional) Maximum number of rows to display in the console.
  • max_cols (int, optional) Maximum number of columns to display in the console.
  • show_dimensions (bool, default False) Display DataFrame dimensions (number of rows by number of columns).
  • decimal (str, default '.') Character recognized as decimal separator, e.g. ',' in Europe.
  • bold_rows (bool, default True) Make the row labels bold in the output.
  • classes (str or list or tuple, default None) CSS class(es) to apply to the resulting html table.
  • escape (bool, default True) Convert the characters <, >, and & to HTML-safe sequences.
  • notebook ({True, False}, default False) Whether the generated HTML is for IPython Notebook.
  • border (int) A border=border attribute is included in the opening<table> tag. Default pd.options.display.html.border.
  • table_id (str, optional) A css id is included in the opening <table> tag if specified.
  • render_links (bool, default False) Convert URLs to HTML links.
  • encoding (str, default "utf-8") Set character encoding.
    .. versionadded:: 1.0
  • min_rows (int, optional) The number of rows to display in the console in a truncated repr(when number of rows is above max_rows).
Returns (str or None)

If buf is None, returns the result as a string. Otherwise returnsNone.

See Also

to_string : Convert DataFrame to a string.

method

to_xml(path_or_buffer=None, index=True, root_name='data', row_name='row', na_rep=None, attr_cols=None, elem_cols=None, namespaces=None, prefix=None, encoding='utf-8', xml_declaration=True, pretty_print=True, parser='lxml', stylesheet=None, compression='infer', storage_options=None)

Render a DataFrame to an XML document.

.. versionadded:: 1.3.0

Parameters
  • path_or_buffer (str, path object or file-like object, optional) File to write output to. If None, the output is returned as astring.
  • index (bool, default True) Whether to include index in XML document.
  • root_name (str, default 'data') The name of root element in XML document.
  • row_name (str, default 'row') The name of row element in XML document.
  • na_rep (str, optional) Missing data representation.
  • attr_cols (list-like, optional) List of columns to write as attributes in row element.Hierarchical columns will be flattened with underscore delimiting the different levels.
  • elem_cols (list-like, optional) List of columns to write as children in row element. By default,all columns output as children of row element. Hierarchical columns will be flattened with underscore delimiting the different levels.
  • namespaces (dict, optional) All namespaces to be defined in root element. Keys of dictshould be prefix names and values of dict corresponding URIs. Default namespaces should be given empty string key. For example, ::
    namespaces = {"": "https://example.com"}
    
  • prefix (str, optional) Namespace prefix to be used for every element and/or attributein document. This should be one of the keys in namespaces dict.
  • encoding (str, default 'utf-8') Encoding of the resulting document.
  • xml_declaration (bool, default True) Whether to include the XML declaration at start of document.
  • pretty_print (bool, default True) Whether output should be pretty printed with indentation andline breaks.
  • parser ({'lxml','etree'}, default 'lxml') Parser module to use for building of tree. Only 'lxml' and'etree' are supported. With 'lxml', the ability to use XSLT stylesheet is supported.
  • stylesheet (str, path object or file-like object, optional) A URL, file-like object, or a raw string containing an XSLTscript used to transform the raw XML output. Script should use layout of elements and attributes from original output. This argument requires lxml to be installed. Only XSLT 1.0 scripts and not later versions is currently supported.
  • compression ({'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer') For on-the-fly decompression of on-disk data. If 'infer', then usegzip, bz2, zip or xz if path_or_buffer is a string ending in '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression.
  • storage_options (dict, optional) Extra options that make sense for a particular storage connection, e.g.host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details.
Returns (None or str)

If io is None, returns the resulting XML format as astring. Otherwise returns None.

See Also

to_json : Convert the pandas object to a JSON string.to_html : Convert DataFrame to a html.

Examples
>>> df = pd.DataFrame({'shape': ['square', 'circle', 'triangle'],...                    'degrees': [360, 360, 180],
...                    'sides': [4, np.nan, 3]})
>>> df.to_xml()  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row>
    <index>0</index>
    <shape>square</shape>
    <degrees>360</degrees>
    <sides>4.0</sides>
  </row>
  <row>
    <index>1</index>
    <shape>circle</shape>
    <degrees>360</degrees>
    <sides/>
  </row>
  <row>
    <index>2</index>
    <shape>triangle</shape>
    <degrees>180</degrees>
    <sides>3.0</sides>
  </row>
</data>
>>> df.to_xml(attr_cols=[
...           'index', 'shape', 'degrees', 'sides'
...           ])  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
  <row index="0" shape="square" degrees="360" sides="4.0"/>
  <row index="1" shape="circle" degrees="360"/>
  <row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>
>>> df.to_xml(namespaces={"doc": "https://example.com"},
...           prefix="doc")  # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
  <doc:row>
    <doc:index>0</doc:index>
    <doc:shape>square</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides>4.0</doc:sides>
  </doc:row>
  <doc:row>
    <doc:index>1</doc:index>
    <doc:shape>circle</doc:shape>
    <doc:degrees>360</doc:degrees>
    <doc:sides/>
  </doc:row>
  <doc:row>
    <doc:index>2</doc:index>
    <doc:shape>triangle</doc:shape>
    <doc:degrees>180</doc:degrees>
    <doc:sides>3.0</doc:sides>
  </doc:row>
</doc:data>
method

info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None, null_counts=None)

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.

Parameters
  • verbose (bool, optional) Whether to print the full summary. By default, the setting inpandas.options.display.max_info_columns is followed.
  • buf (writable buffer, defaults to sys.stdout) Where to send the output. By default, the output is printed tosys.stdout. Pass a writable buffer if you need to further process the output.
  • max_cols (int, optional) When to switch from the verbose to the truncated output. If theDataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.
  • memory_usage (bool, str, optional) Specifies whether total memory usage of the DataFrameelements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting.
    True always show memory usage. False never shows memory usage. A value of 'deep' is equivalent to "True with deep introspection". Memory usage is shown in human-readable units (base-2 representation). Without deep introspection a memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes. With deep memory introspection, a real memory usage calculation is performed at the cost of computational resources.
  • show_counts (bool, optional) Whether to show the non-null counts. By default, this is shownonly if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.
  • null_counts (bool, optional) .. deprecated:: 1.2.0 Use show_counts instead.
  • data (DataFrame) DataFrame to print information about.
Returns (None)

This method prints a summary of a DataFrame and returns None.

See Also

DataFrame.describe: Generate descriptive statistics of DataFrame columns. DataFrame.memory_usage: Memory usage of DataFrame columns.

Examples
>>> int_values = [1, 2, 3, 4, 5]>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
...                   "float_col": float_values})
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Prints a summary of columns count and its dtypes but not per column information:

>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  # doctest: +SKIP
...     f.write(s)
260

The memory_usage parameter allows deep introspection mode, specially useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 165.9 MB
method

memory_usage(index=True, deep=False)

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameters
  • index (bool, default True) Specifies whether to include the memory usage of the DataFrame'sindex in returned Series. If index=True, the memory usage of the index is the first item in the output.
  • deep (bool, default False) If True, introspect the data deeply by interrogatingobject dtypes for system-level memory consumption, and include it in the returned values.
Returns (Series)

A Series whose index is the original column names and whose valuesis the memory usage of each column in bytes.

See Also

numpy.ndarray.nbytes : Total bytes consumed by the elements of an ndarray. Series.memory_usage : Bytes consumed by a Series. Categorical : Memory-efficient array for string values with many repeated values. DataFrame.info : Concise summary of a DataFrame.

Examples
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
...              for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
   int64  float64            complex128  object  bool
0      1      1.0              1.0+0.0j       1  True
1      1      1.0              1.0+0.0j       1  True
2      1      1.0              1.0+0.0j       1  True
3      1      1.0              1.0+0.0j       1  True
4      1      1.0              1.0+0.0j       1  True
>>> df.memory_usage()
Index           128
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64
>>> df.memory_usage(index=False)
int64         40000
float64       40000
complex128    80000
object        40000
bool           5000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

>>> df.memory_usage(deep=True)
Index            128
int64          40000
float64        40000
complex128     80000
object        180000
bool            5000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

>>> df['object'].astype('category').memory_usage(deep=True)
5244
method

transpose(*args, copy=False)

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property :attr:.T is an accessor to the method :meth:transpose.

Parameters
  • *args (tuple, optional) Accepted for compatibility with NumPy.
  • copy (bool, default False) Whether to copy the data after transposing, even for DataFrameswith a single dtype.
    Note that a copy is always required for mixed dtype DataFrames, or for DataFrames with any extension types.
Returns (DataFrame)

The transposed DataFrame.

See Also

numpy.transpose : Permute the dimensions of a given array.

Notes

Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the object dtype. In such a case, a copy of the data is always made.

Examples

Square DataFrame with homogeneous dtype

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4

When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    int64
col2    int64
dtype: object
>>> df1_transposed.dtypes
0    int64
1    int64
dtype: object

Non-square DataFrame with mixed dtypes

>>> d2 = {'name': ['Alice', 'Bob'],
...       'score': [9.5, 8],
...       'employed': [False, True],
...       'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
    name  score  employed  kids
0  Alice    9.5     False     0
1    Bob    8.0      True     0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
              0     1
name      Alice   Bob
score       9.5   8.0
employed  False  True
kids          0     0

When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype:

>>> df2.dtypes
name         object
score       float64
employed       bool
kids          int64
dtype: object
>>> df2_transposed.dtypes
0    object
1    object
dtype: object
method

query(expr, inplace=False, **kwargs)

Query the columns of a DataFrame with a boolean expression.

Parameters
  • expr (str) The query string to evaluate.
    You can refer to variables in the environment by prefixing them with an '@' character like @a + b.
    You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named "Area (cm^2)" would be referenced as Area (cm^2)). Column names which are Python keywords (like "list", "for", "import", etc) cannot be used.
    For example, if one of your columns is called a a and you want to sum it with b, your query should be `a a` + b.
    .. versionadded:: 0.25.0 Backtick quoting introduced.
    .. versionadded:: 1.0.0 Expanding functionality of backtick quoting for more than only spaces.
  • inplace (bool) Whether the query should modify the data in place or returna modified copy.
  • **kwargs See the documentation for :func:eval for complete detailson the keyword arguments accepted by :meth:DataFrame.query.
Returns (DataFrame or None)

DataFrame resulting from the provided query expression orNone if inplace=True.

See Also

eval : Evaluate a string describing operations on DataFrame columns. DataFrame.eval : Evaluate a string describing operations on DataFrame columns.

Notes

The result of the evaluation of this expression is first passed to :attr:DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to :meth:DataFrame.__getitem__.

This method uses the top-level :func:eval function to evaluate the passed query.

The :meth:~pandas.DataFrame.query method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, :keyword:and and :keyword:or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The :attr:DataFrame.index and :attr:DataFrame.columns attributes of the :class:~pandas.DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query. Please note that Python keywords may not be used as identifiers.

For further details and examples see the query documentation in :ref:indexing <indexing.query>.

Backtick quoted variables

Backtick quoted variables are parsed as literal Python code and are converted internally to a Python valid identifier. This can lead to the following problems.

During parsing a number of disallowed characters inside the backtick quoted string are replaced by strings that are allowed as a Python identifier. These characters include all operators in Python, the space character, the question mark, the exclamation mark, the dollar sign, and the euro sign. For other characters that fall outside the ASCII range (U+0001..U+007F) and those that are not further specified in PEP 3131, the query parser will raise an error. This excludes whitespace different than the space character, but also the hashtag (as it is used for comments) and the backtick itself (backtick can also not be escaped).

In a special case, quotes that make a pair around a backtick can confuse the parser. For example, it's` > `that's will raise an error, as it forms a quoted string ('s > `that') with a backtick inside.

See also the Python documentation about lexical analysis (https://docs.python.org/3/reference/lexical_analysis.html) in combination with the source code in :mod:pandas.core.computation.parsing.

Examples
>>> df = pd.DataFrame({'A': range(1, 6),...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
   A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
>>> df.query('A > B')
   A  B  C C
4  5  2    6

The previous expression is equivalent to

>>> df[df.A > df.B]
   A  B  C C
4  5  2    6

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
   A   B  C C
0  1  10   10

The previous expression is equivalent to

>>> df[df.B == df['C C']]
   A   B  C C
0  1  10   10
method

eval(expr, inplace=False, **kwargs)

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Parameters
  • expr (str) The expression string to evaluate.
  • inplace (bool, default False) If the expression contains an assignment, whether to perform theoperation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.
  • **kwargs See the documentation for :func:eval for complete detailson the keyword arguments accepted by :meth:~pandas.DataFrame.query.
Returns (ndarray, scalar, pandas object, or None)

The result of the evaluation or None if inplace=True.

See Also

DataFrame.query : Evaluates a boolean expression to query the columns of a frame. DataFrame.assign : Can evaluate an expression or function to create new values for a column. eval : Evaluate a Python expression as a string using various backends.

Notes

For more details see the API documentation for :func:~eval. For detailed examples see :ref:enhancing performance with eval <enhancingperf.eval>.

Examples
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2

Use inplace=True to modify the original DataFrame.

>>> df.eval('C = A + B', inplace=True)
>>> df
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3
method

select_dtypes(include=None, exclude=None)

Return a subset of the DataFrame's columns based on the column dtypes.

Returns (DataFrame)

The subset of the frame including the dtypes in include andexcluding the dtypes in exclude.

Raises
  • ValueError
    • If both of include and exclude are empty
    • If include and exclude have overlapping elements
    • If any kind of string dtype is passed in.
See Also

DataFrame.dtypes: Return Series with the data type of each column.

Notes

  • To select all numeric types, use np.number or 'number'
  • To select strings you must use the object dtype, but note that this will return all object dtype columns
  • See the numpy dtype hierarchy <https://numpy.org/doc/stable/reference/arrays.scalars.html>__
  • To select datetimes, use np.datetime64, 'datetime' or 'datetime64'
  • To select timedeltas, use np.timedelta64, 'timedelta' or 'timedelta64'
  • To select Pandas categorical dtypes, use 'category'
  • To select Pandas datetimetz dtypes, use 'datetimetz' (new in 0.20.0) or 'datetime64[ns, tz]'
Examples
>>> df = pd.DataFrame({'a': [1, 2] * 3,...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0       1   True  1.0
1       2  False  2.0
2       1   True  1.0
3       2  False  2.0
4       1   True  1.0
5       2  False  2.0
>>> df.select_dtypes(include='bool')
   b
0  True
1  False
2  True
3  False
4  True
5  False
>>> df.select_dtypes(include=['float64'])
   c
0  1.0
1  2.0
2  1.0
3  2.0
4  1.0
5  2.0
>>> df.select_dtypes(exclude=['int64'])
       b    c
0   True  1.0
1  False  2.0
2   True  1.0
3  False  2.0
4   True  1.0
5  False  2.0
method

insert(loc, column, value, allow_duplicates=False)

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

Parameters
  • loc (int) Insertion index. Must verify 0 <= loc <= len(columns).
  • column (str, number, or hashable object) Label of the inserted column.
See Also

Index.insert : Insert new item by index.

Examples
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})>>> df
   col1  col2
0     1     3
1     2     4
>>> df.insert(1, "newcol", [99, 99])
>>> df
   col1  newcol  col2
0     1      99     3
1     2      99     4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
   col1  col1  newcol  col2
0   100     1      99     3
1   100     2      99     4

Notice that pandas uses index alignment in case of value from type Series:

>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
   col0  col1  col1  newcol  col2
0   NaN   100     1      99     3
1   5.0   100     2      99     4
method

assign(**kwargs)

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters
  • **kwargs (dict of {str: callable or Series}) The column names are keywords. If the values arecallable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn't check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
Returns (DataFrame)

A new DataFrame with the new columns in addition toall the existing columns.

Notes

Assigning multiple columns within the same assign is possible. Later items in '**kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order.

Examples
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] +  459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15
method

lookup(row_labels, col_labels)

Label-based "fancy indexing" function for DataFrame.Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair.

.. deprecated:: 1.2.0 DataFrame.lookup is deprecated, use DataFrame.melt and DataFrame.loc instead. For further details see :ref:Looking up values by index/column labels <indexing.lookup>.

Parameters
  • row_labels (sequence) The row labels to use for lookup.
  • col_labels (sequence) The column labels to use for lookup.
Returns (numpy.ndarray)

The found values.

method

align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters
  • axis (allowed axis of the other object, default None) Align on index (0), columns (1), or both (None).
  • level (int or level name, default None) Broadcast across a level, matching Index values on thepassed MultiIndex level.
  • copy (bool, default True) Always returns new objects. If copy=False and no reindexing isrequired then original objects are returned.
  • fill_value (scalar, default np.NaN) Value to use for missing values. Defaults to NaN, but can be any"compatible" value.
  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) Method to use for filling holes in reindexed Series:
    • pad / ffill: propagate last valid observation forward to next valid.
    • backfill / bfill: use NEXT valid observation to fill gap.
  • limit (int, default None) If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
  • fill_axis ({0 or 'index', 1 or 'columns'}, default 0) Filling axis, method and limit.
  • broadcast_axis ({0 or 'index', 1 or 'columns'}, default None) Broadcast values along this axis, if aligning two objects ofdifferent dimensions.
Returns (left, right) : (DataFrame, type of other)

Aligned objects.

method

set_axis(labels, axis=0, inplace=False)

Assign desired index to given axis.

Indexes for column or row labels can be changed by assigning a list-like or Index.

Parameters
  • labels (list-like, Index) The values for the new index.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to update. The value 0 identifies the rows, and 1 identifies the columns.
  • inplace (bool, default False) Whether to return a new DataFrame instance.
Returns (renamed : DataFrame or None)

An object of type DataFrame or None if inplace=True.

See Also

DataFrame.renameaxis : Alter the name of the index or columns.

    Examples
    --------

~~~python

df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) ~~~

    Change the row labels.
>>> df.set_axis(['a', 'b', 'c'], axis='index')
   A  B
a  1  4
b  2  5
c  3  6
    Change the column labels.
>>> df.set_axis(['I', 'II'], axis='columns')
   I  II
0  1   4
1  2   5
2  3   6
    Now, update the labels inplace.
>>> df.set_axis(['i', 'ii'], axis='columns', inplace=True)
>>> df
   i  ii
0  1   4
1  2   5
2  3   6
method

drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

Drop specified labels from rows or columns.

Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide <advanced.shown_levels> for more information about the now unused levels.

Parameters
  • labels (single label or list-like) Index or column labels to drop.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Whether to drop labels from the index (0 or 'index') orcolumns (1 or 'columns').
  • index (single label or list-like) Alternative to specifying axis (labels, axis=0is equivalent to index=labels).
  • columns (single label or list-like) Alternative to specifying axis (labels, axis=1is equivalent to columns=labels).
  • level (int or level name, optional) For MultiIndex, level from which the labels will be removed.
  • inplace (bool, default False) If False, return a copy. Otherwise, do operationinplace and return None.
  • errors ({'ignore', 'raise'}, default 'raise') If 'ignore', suppress error and only existing labels aredropped.
Returns (DataFrame or None)

DataFrame without the removed index or column labels orNone if inplace=True.

Raises
  • KeyError If any of the labels is not found in the selected axis.
See Also

DataFrame.loc : Label-location based indexer for selection by label.DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. DataFrame.dropduplicates : Return DataFrame with duplicate rows removed, optionally only considering certain columns. Series.drop : Return Series with specified index labels removed.

Examples
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Drop columns and/or rows of MultiIndex DataFrame

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                         [250, 150], [1.5, 0.8], [320, 250],
...                         [1, 0.8], [0.3, 0.2]])
>>> df
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
        length  1.5     1.0
cow     speed   30.0    20.0
        weight  250.0   150.0
        length  1.5     0.8
falcon  speed   320.0   250.0
        weight  1.0     0.8
        length  0.3     0.2
>>> df.drop(index='cow', columns='small')
                big
lama    speed   45.0
        weight  200.0
        length  1.5
falcon  speed   320.0
        weight  1.0
        length  0.3
>>> df.drop(index='length', level=1)
                big     small
lama    speed   45.0    30.0
        weight  200.0   100.0
cow     speed   30.0    20.0
        weight  250.0   150.0
falcon  speed   320.0   250.0
        weight  1.0     0.8
method

rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore')

Alter axes labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error.

See the :ref:user guide <basics.rename> for more.

Parameters
  • mapper (dict-like or function) Dict-like or function transformations to apply tothat axis' values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.
  • index (dict-like or function) Alternative to specifying axis (mapper, axis=0is equivalent to index=mapper).
  • columns (dict-like or function) Alternative to specifying axis (mapper, axis=1is equivalent to columns=mapper).
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis to target with mapper. Can be either the axis name('index', 'columns') or number (0, 1). The default is 'index'.
  • copy (bool, default True) Also copy underlying data.
  • inplace (bool, default False) Whether to return a new DataFrame. If True then value of copy isignored.
  • level (int or level name, default None) In case of a MultiIndex, only rename labels in the specifiedlevel.
  • errors ({'ignore', 'raise'}, default 'ignore') If 'raise', raise a KeyError when a dict-like mapper, index,or columns contains labels that are not present in the Index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored.
Returns (DataFrame or None)

DataFrame with the renamed axis labels or None if inplace=True.

Raises
  • KeyError If any of the labels is not found in the selected axis and"errors='raise'".
See Also

DataFrame.renameaxis : Set the name of the axis.

Examples

DataFrame.rename supports two calling conventions

  • (index=index_mapper, columns=columns_mapper, ...)
  • (mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Rename columns using a mapping:

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

Rename index using a mapping:

>>> df.rename(index={0: "x", 1: "y", 2: "z"})
   A  B
x  1  4
y  2  5
z  3  6

Cast index labels to a different type:

>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis

Using axis-style parameters:

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6
>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6
method

fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)

Fill NA/NaN values using the specified method.

Parameters
  • value (scalar, dict, Series, or DataFrame) Value to use to fill holes (e.g. 0), alternately adict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.
  • method ({'backfill', 'bfill', 'pad', 'ffill', None}, default None) Method to use for filling holes in reindexed Seriespad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap.
  • axis ({0 or 'index', 1 or 'columns'}) Axis along which to fill missing values.
  • inplace (bool, default False) If True, fill in-place. Note: this will modify anyother views on this object (e.g., a no-copy slice for a column in a DataFrame).
  • limit (int, default None) If method is specified, this is the maximum number of consecutiveNaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.
  • downcast (dict, default is None) A dict of item->dtype of what to downcast if possible,or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).
Returns (DataFrame or None)

Object with missing values filled or None if inplace=True.

See Also

interpolate : Fill NaN values using interpolation.reindex : Conform object to new index. asfreq : Convert TimeSeries to specified frequency.

Examples
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, 5],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

Replace all NaN elements with 0s.

>>> df.fillna(0)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4

We can also propagate non-null values forward or backward.

>>> df.fillna(method="ffill")
    A   B   C   D
0   NaN 2.0 NaN 0
1   3.0 4.0 NaN 1
2   3.0 4.0 NaN 5
3   3.0 3.0 NaN 4

Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 2.0 1
2   0.0 1.0 2.0 5
3   0.0 3.0 2.0 4

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 NaN 1
2   NaN 1.0 NaN 5
3   NaN 3.0 NaN 4

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4
method

pop(item)

Return item and drop from frame. Raise KeyError if not found.

Parameters
  • item (label) Label of column to be popped.
Examples
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),...                    ('parrot', 'bird', 24.0),
...                    ('lion', 'mammal', 80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN
>>> df.pop('class')
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object
>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN
method

replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')

Replace values given in to_replace with value.

Values of the DataFrame are replaced with other values dynamically.

This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Parameters
  • to_replace (str, regex, list, dict, Series, int, float, or None) How to find the values that will be replaced.
    • numeric, str or regex:
      • numeric: numeric values equal to to_replace will be replaced with value
      • str: string exactly matching to_replace will be replaced with value
      • regex: regexs matching to_replace will be replaced with value
    • list of str, regex, or numeric:
      • First, if to_replace and value are both lists, they must be the same length.
      • Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for value since there are only a few possible substitution regexes you can use.
      • str, regex and numeric rules apply as above.
    • dict:
      • Dicts can be used to specify different replacement values for different existing values. For example, {'a': 'b', 'y': 'z'} replaces the value 'a' with 'b' and 'y' with 'z'. To use a dict in this way the value parameter should be None.
      • For a DataFrame a dict can specify that different values should be replaced in different columns. For example, {'a': 1, 'b': 'z'} looks for the value 1 in column 'a' and the value 'z' in column 'b' and replaces these values with whatever is specified in value. The value parameter should not be None in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in.
      • For a DataFrame nested dictionaries, e.g., {'a': {'b': np.nan}}, are read as follows: look in column 'a' for the value 'b' and replace it with NaN. The value parameter should be None to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
    • None:
      • This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.
    See the examples section for examples of each of these.
  • value (scalar, dict, list, str, regex, default None) Value to replace any values matching to_replace with.For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.
  • inplace (bool, default False) If True, performs operation inplace and returns None.
  • limit (int, default None) Maximum size gap to forward or backward fill.
  • regex (bool or same types as `to_replace`, default False) Whether to interpret to_replace and/or value as regularexpressions. If this is True then to_replace must be a string. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case to_replace must be None.
  • method ({'pad', 'ffill', 'bfill', `None`}) The method to use when for replacement, when to_replace is ascalar, list or tuple and value is None.
    .. versionchanged:: 0.23.0 Added to DataFrame.
Returns (DataFrame)

Object after replacement.

Raises
  • AssertionError
    • If regex is not a bool and to_replace is not None.
  • TypeError
    • If to_replace is not a scalar, array-like, dict, or None
    • If to_replace is a dict and value is not a list, dict, ndarray, or Series
    • If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series.
    • When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced
  • ValueError
    • If a list or an ndarray is passed to to_replace and value but they are not the same length.
See Also

DataFrame.fillna : Fill NA values.DataFrame.where : Replace values based on boolean condition. Series.str.replace : Simple string replacement.

Notes

  • Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
  • Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
  • This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.
  • When dict is used as the to_replace value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter.
Examples

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method

shift(periods=1, freq=None, axis=0, fill_value=<no_default>)

Shift index by desired number of periods with an optional time freq.

When freq is not passed, shift the index without realigning the data. If freq is passed (in this case, the index must be date or datetime, or it will raise a NotImplementedError), the index will be increased using the periods and the freq. freq can be inferred when specified as "infer" as long as either freq or inferred_freq attribute is set in the index.

Parameters
  • periods (int) Number of periods to shift. Can be positive or negative.
  • freq (DateOffset, tseries.offsets, timedelta, or str, optional) Offset to use from the tseries module or time rule (e.g. 'EOM').If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data. If freq is specified as "infer" then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
  • axis ({0 or 'index', 1 or 'columns', None}, default None) Shift direction.
  • fill_value (object, optional) The scalar value to use for newly introduced missing values.the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. :attr:NaT is used. For extension dtypes, self.dtype.na_value is used.
    .. versionchanged:: 1.1.0
Returns (DataFrame)

Copy of input object, shifted.

See Also

Index.shift : Shift values of Index.DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex. tshift : Shift the time index, using the index's frequency if available.

Examples
>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],...                    "Col2": [13, 23, 18, 33, 48],
...                    "Col3": [17, 27, 22, 37, 52]},
...                   index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52
>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0
>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01   NaN    10    13
2020-01-02   NaN    20    23
2020-01-03   NaN    15    18
2020-01-04   NaN    30    33
2020-01-05   NaN    45    48
>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27
>>> df.shift(periods=3, freq="D")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
>>> df.shift(periods=3, freq="infer")
            Col1  Col2  Col3
2020-01-04    10    13    17
2020-01-05    20    23    27
2020-01-06    15    18    22
2020-01-07    30    33    37
2020-01-08    45    48    52
method

set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.

Parameters
  • keys (label or array-like or list of labels/arrays) This parameter can be either a single column key, a single array ofthe same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, "array" encompasses :class:Series, :class:Index, np.ndarray, and instances of :class:~collections.abc.Iterator.
  • drop (bool, default True) Delete columns to be used as the new index.
  • append (bool, default False) Whether to append columns to existing index.
  • inplace (bool, default False) If True, modifies the DataFrame in place (do not create a new object).
  • verify_integrity (bool, default False) Check the new index for duplicates. Otherwise defer the check untilnecessary. Setting to False will improve the performance of this method.
Returns (DataFrame or None)

Changed row labels or None if inplace=True.

See Also

DataFrame.reset_index : Opposite of set_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.

Examples
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],...                    'year': [2012, 2014, 2013, 2014],
...                    'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31

Set the index to become the 'month' column:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31

Create a MultiIndex using columns 'year' and 'month':

>>> df.set_index(['year', 'month'])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a MultiIndex using an Index and a column:

>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
      month  year  sale
1 1       1  2012    55
2 4       4  2014    40
3 9       7  2013    84
4 16     10  2014    31
method

reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')

Reset the index, or a level of it.

Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more levels.

Parameters
  • level (int, str, tuple, or list, default None) Only remove the given levels from the index. Removes all levels bydefault.
  • drop (bool, default False) Do not try to insert index into dataframe columns. This resetsthe index to the default integer index.
  • inplace (bool, default False) Modify the DataFrame in place (do not create a new object).
  • col_level (int or str, default 0) If the columns have multiple levels, determines which level thelabels are inserted into. By default it is inserted into the first level.
  • col_fill (object, default '') If the columns have multiple levels, determines how the otherlevels are named. If None then the index name is repeated.
Returns (DataFrame or None)

DataFrame with the new index or None if inplace=True.

See Also

DataFrame.set_index : Opposite of reset_index.DataFrame.reindex : Change to new indices or expand indices. DataFrame.reindexlike : Change to same indices as other DataFrame.

Examples
>>> df = pd.DataFrame([('bird', 389.0),...                    ('bird', 24.0),
...                    ('mammal', 80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
...                    ( 24.0, 'fly'),
...                    ( 80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump
method

isna()

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.

See Also

DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
method

isnull()

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is an NA value.

See Also

DataFrame.isnull : Alias of isna.DataFrame.notna : Boolean inverse of isna. DataFrame.dropna : Omit axes labels with missing values. isna : Top-level isna.

Examples

Show which entries in a DataFrame are NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.isna()
     age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False

Show which entries in a Series are NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.isna()
0    False
1    False
2     True
dtype: bool
method

notna()

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or :attr:numpy.NaN, get mapped to False values.

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.

See Also

DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
method

notnull()

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or :attr:numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or :attr:numpy.NaN, get mapped to False values.

Returns (DataFrame)

Mask of bool values for each element in DataFrame thatindicates whether an element is not an NA value.

See Also

DataFrame.notnull : Alias of notna.DataFrame.isna : Boolean inverse of notna. DataFrame.dropna : Omit axes labels with missing values. notna : Top-level notna.

Examples

Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
...                    born=[pd.NaT, pd.Timestamp('1939-05-27'),
...                          pd.Timestamp('1940-04-25')],
...                    name=['Alfred', 'Batman', ''],
...                    toy=[None, 'Batmobile', 'Joker']))
>>> df
   age       born    name        toy
0  5.0        NaT  Alfred       None
1  6.0 1939-05-27  Batman  Batmobile
2  NaN 1940-04-25              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are not NA.

>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0    5.0
1    6.0
2    NaN
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
dtype: bool
method

dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Remove missing values.

See the :ref:User Guide <missing_data> for more on which values are considered missing, and how to work with missing data.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Determine if rows or columns which contain missing values areremoved.
    • 0, or 'index' : Drop rows which contain missing values.
    • 1, or 'columns' : Drop columns which contain missing value.
    .. versionchanged:: 1.0.0
    Pass tuple or list to drop on multiple axes. Only a single axis is allowed.
  • how ({'any', 'all'}, default 'any') Determine if row or column is removed from DataFrame, when we haveat least one NA or all NA.
    • 'any' : If any NA values are present, drop that row or column.
    • 'all' : If all values are NA, drop that row or column.
  • thresh (int, optional) Require that many non-NA values.
  • subset (array-like, optional) Labels along other axis to consider, e.g. if you are dropping rowsthese would be a list of columns to include.
  • inplace (bool, default False) If True, do operation inplace and return None.
Returns (DataFrame or None)

DataFrame with NA entries dropped from it or None if inplace=True.

See Also

DataFrame.isna: Indicate missing values.DataFrame.notna : Indicate existing (non-missing) values. DataFrame.fillna : Replace missing values. Series.dropna : Drop missing values. Index.dropna : Drop missing indices.

Examples
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],...                    "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                    "born": [pd.NaT, pd.Timestamp("1940-04-25"),
...                             pd.NaT]})
>>> df
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Drop the rows where at least one element is missing.

>>> df.dropna()
     name        toy       born
1  Batman  Batmobile 1940-04-25

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman

Drop the rows where all elements are missing.

>>> df.dropna(how='all')
       name        toy       born
0    Alfred        NaN        NaT
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep only the rows with at least 2 non-NA values.

>>> df.dropna(thresh=2)
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])
       name        toy       born
1    Batman  Batmobile 1940-04-25
2  Catwoman   Bullwhip        NaT

Keep the DataFrame with valid entries in the same variable.

>>> df.dropna(inplace=True)
>>> df
     name        toy       born
1  Batman  Batmobile 1940-04-25
method

drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False)

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters
  • subset (column label or sequence of labels, optional) Only consider certain columns for identifying duplicates, bydefault use all of the columns.
  • keep ({'first', 'last', False}, default 'first') Determines which duplicates (if any) to keep.
    • - first : Drop duplicates except for the first occurrence.
    • - last : Drop duplicates except for the last occurrence.
    • - False : Drop all duplicates.
  • inplace (bool, default False) Whether to drop duplicates in place or to return a copy.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
Returns (DataFrame or None)

DataFrame with duplicates removed or None if inplace=True.

See Also

DataFrame.value_counts: Count unique combinations of columns.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])
    brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
    brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0
method

duplicated(subset=None, keep='first')

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters
  • subset (column label or sequence of labels, optional) Only consider certain columns for identifying duplicates, bydefault use all of the columns.
  • keep ({'first', 'last', False}, default 'first') Determines which duplicates (if any) to mark.
    • first : Mark duplicates as True except for the first occurrence.
    • last : Mark duplicates as True except for the last occurrence.
    • False : Mark all duplicates as True.
Returns (Series)

Boolean series for each duplicated rows.

See Also

Index.duplicated : Equivalent method on index.Series.duplicated : Equivalent method on Series. Series.dropduplicates : Remove duplicate values from Series. DataFrame.dropduplicates : Remove duplicate values from DataFrame.

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
    brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, for each set of duplicated values, the first occurrence is set on False and all others on True.

>>> df.duplicated()
0    False
1     True
2    False
3    False
4    False
dtype: bool

By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True.

>>> df.duplicated(keep='last')
0     True
1    False
2    False
3    False
4    False
dtype: bool

By setting keep on False, all duplicates are True.

>>> df.duplicated(keep=False)
0     True
1     True
2    False
3    False
4    False
dtype: bool

To find duplicates on specific column(s), use subset.

>>> df.duplicated(subset=['brand'])
0    False
1     True
2    False
3     True
4     True
dtype: bool
method

sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last', ignore_index=False, key=None)

Sort by the values along either axis.

Parameters
  • axis (str or int, optional) Axis to be sorted.
  • ascending (optional) Sort ascending vs. descending. Specify list for multiple sortorders. If this is a list of bools, must match the length of the by.
  • inplace (bool, optional) If True, perform operation in-place.
  • kind (str, optional) Choice of sorting algorithm. See also :func:numpy.sort for moreinformation. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
  • na_position (str, optional) Puts NaNs at the beginning if first; last puts NaNs at theend.
  • ignore_index (bool, optional) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
  • key (callable(Series: Series, ExtensionArray, ndarray, or Index), optional) Apply the key function to the valuesbefore sorting. This is similar to the key argument in the builtin :meth:sorted function, with the notable difference that this key function should be vectorized. It should expect a Series and return a Series with the same shape as the input. It will be applied to each column in by independently.
    .. versionadded:: 1.1.0
  • by (str or list of str) Name or list of names to sort by.
    • if axis is 0 or 'index' then by may contain index levels and/or column labels.
    • if axis is 1 or 'columns' then by may contain column levels and/or index labels.
Returns (DataFrame or None)

DataFrame with sorted values or None if inplace=True.

See Also

DataFrame.sort_index : Sort a DataFrame by the index.Series.sort_values : Similar method for a Series.

Examples
>>> df = pd.DataFrame({...     'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Sort by col1

>>> df.sort_values(by=['col1'])
  col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
  col1  col2  col3 col4
1    A     1     1    B
0    A     2     0    a
2    B     9     9    c
5    C     4     3    F
4    D     7     2    e
3  NaN     8     4    D

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
  col1  col2  col3 col4
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B
3  NaN     8     4    D

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
  col1  col2  col3 col4
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F
2    B     9     9    c
0    A     2     0    a
1    A     1     1    B

Sorting with a key function

>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
   col1  col2  col3 col4
0    A     2     0    a
1    A     1     1    B
2    B     9     9    c
3  NaN     8     4    D
4    D     7     2    e
5    C     4     3    F

Natural sort with the key argument, using the natsort <https://github.com/SethMMorton/natsort> package.

>>> df = pd.DataFrame({
...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
...    "value": [10, 20, 30, 40, 50]
... })
>>> df
    time  value
0    0hr     10
1  128hr     20
2   72hr     30
3   48hr     40
4   96hr     50
>>> from natsort import index_natsorted
>>> df.sort_values(
...    by="time",
...    key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
    time  value
0    0hr     10
3   48hr     40
2   72hr     30
4   96hr     50
1  128hr     20
method

sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)

Sort object by labels (along an axis).

Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis along which to sort. The value 0 identifies the rows,and 1 identifies the columns.
  • level (int or level name or list of ints or list of level names) If not None, sort on values in specified index level(s).
  • ascending (bool or list-like of bools, default True) Sort ascending vs. descending. When the index is a MultiIndex thesort direction can be controlled for each level individually.
  • inplace (bool, default False) If True, perform operation in-place.
  • kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') Choice of sorting algorithm. See also :func:numpy.sort for moreinformation. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
  • na_position ({'first', 'last'}, default 'last') Puts NaNs at the beginning if first; last puts NaNs at the end.Not implemented for MultiIndex.
  • sort_remaining (bool, default True) If True and sorting by level and index is multilevel, sort by otherlevels too (in order) after sorting by specified level.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.0.0
  • key (callable, optional) If not None, apply the key function to the index valuesbefore sorting. This is similar to the key argument in the builtin :meth:sorted function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.
    .. versionadded:: 1.1.0
Returns (DataFrame or None)

The original DataFrame sorted by the labels or None if inplace=True.

See Also

Series.sort_index : Sort Series by the index.DataFrame.sort_values : Sort DataFrame by the value. Series.sort_values : Sort Series by the value.

Examples
>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],...                   columns=['A'])
>>> df.sort_index()
     A
1    4
29   2
100  1
150  5
234  3

By default, it sorts in ascending order, to sort in descending order, use ascending=False

>>> df.sort_index(ascending=False)
     A
234  3
150  5
100  1
29   2
1    4

A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately.

>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
   a
A  1
b  2
C  3
d  4
method

value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)

Return a Series containing counts of unique rows in the DataFrame.

.. versionadded:: 1.1.0

Parameters
  • subset (list-like, optional) Columns to use when counting unique combinations.
  • normalize (bool, default False) Return proportions rather than frequencies.
  • sort (bool, default True) Sort by frequencies.
  • ascending (bool, default False) Sort in ascending order.
  • dropna (bool, default True) Don’t include counts of rows that contain NA values.
    .. versionadded:: 1.3.0
See Also

Series.value_counts: Equivalent method on Series.

Notes

The returned Series will have a MultiIndex with one level per input column. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],...                    'num_wings': [2, 0, 0, 0]},
...                   index=['falcon', 'dog', 'cat', 'ant'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0
cat            4          0
ant            6          0
>>> df.value_counts()
num_legs  num_wings
4         0            2
2         2            1
6         0            1
dtype: int64
>>> df.value_counts(sort=False)
num_legs  num_wings
2         2            1
4         0            2
6         0            1
dtype: int64
>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2            1
6         0            1
4         0            2
dtype: int64
>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0            0.50
2         2            0.25
6         0            0.25
dtype: float64

With dropna set to False we can also count rows with NA values.

>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
...                    'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
>>> df
  first_name middle_name
0       John       Smith
1       Anne        <NA>
2       John        <NA>
3       Beth      Louise
>>> df.value_counts()
first_name  middle_name
Beth        Louise         1
John        Smith          1
dtype: int64
>>> df.value_counts(dropna=False)
first_name  middle_name
Anne        NaN            1
Beth        Louise         1
John        Smith          1
            NaN            1
dtype: int64
method

nlargest(n, columns, keep='first')

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Parameters
  • n (int) Number of rows to return.
  • columns (label or list of labels) Column label(s) to order by.
  • keep ({'first', 'last', 'all'}, default 'first') Where there are duplicate values:
    • first : prioritize the first occurrence(s)
    • last : prioritize the last occurrence(s)
    • all : do not drop any duplicates, even it means selecting more than n items.
Returns (DataFrame)

The first n rows ordered by the given columns in descendingorder.

See Also

DataFrame.nsmallest : Return the first n rows ordered by columns in ascending order. DataFrame.sort_values : Sort DataFrame by the values. DataFrame.head : Return the first n rows without re-ordering.

Notes

This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,...                                   434000, 434000, 337000, 11300,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru          11300      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nlargest to select the three rows having the largest values in column "population".

>>> df.nlargest(3, 'population')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Malta       434000    12011      MT

When using keep='last', ties are resolved in reverse order:

>>> df.nlargest(3, 'population', keep='last')
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN

When using keep='all', all duplicate items are maintained:

>>> df.nlargest(3, 'population', keep='all')
          population      GDP alpha-2
France      65000000  2583560      FR
Italy       59000000  1937894      IT
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN

To order by the largest values in column "population" and then "GDP", we can specify multiple columns like in the next example.

>>> df.nlargest(3, ['population', 'GDP'])
        population      GDP alpha-2
France    65000000  2583560      FR
Italy     59000000  1937894      IT
Brunei      434000    12128      BN
method

nsmallest(n, columns, keep='first')

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Parameters
  • n (int) Number of items to retrieve.
  • columns (list or str) Column name or names to order by.
  • keep ({'first', 'last', 'all'}, default 'first') Where there are duplicate values:
    • first : take the first occurrence.
    • last : take the last occurrence.
    • all : do not drop any duplicates, even it means selecting more than n items.
See Also

DataFrame.nlargest : Return the first n rows ordered by columns in descending order. DataFrame.sort_values : Sort DataFrame by the values. DataFrame.head : Return the first n rows without re-ordering.

Examples
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,...                                   434000, 434000, 337000, 337000,
...                                   11300, 11300],
...                    'GDP': [1937894, 2583560 , 12011, 4520, 12128,
...                            17036, 182, 38, 311],
...                    'alpha-2': ["IT", "FR", "MT", "MV", "BN",
...                                "IS", "NR", "TV", "AI"]},
...                   index=["Italy", "France", "Malta",
...                          "Maldives", "Brunei", "Iceland",
...                          "Nauru", "Tuvalu", "Anguilla"])
>>> df
          population      GDP alpha-2
Italy       59000000  1937894      IT
France      65000000  2583560      FR
Malta         434000    12011      MT
Maldives      434000     4520      MV
Brunei        434000    12128      BN
Iceland       337000    17036      IS
Nauru         337000      182      NR
Tuvalu         11300       38      TV
Anguilla       11300      311      AI

In the following example, we will use nsmallest to select the three rows having the smallest values in column "population".

>>> df.nsmallest(3, 'population')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS

When using keep='last', ties are resolved in reverse order:

>>> df.nsmallest(3, 'population', keep='last')
          population  GDP alpha-2
Anguilla       11300  311      AI
Tuvalu         11300   38      TV
Nauru         337000  182      NR

When using keep='all', all duplicate items are maintained:

>>> df.nsmallest(3, 'population', keep='all')
          population    GDP alpha-2
Tuvalu         11300     38      TV
Anguilla       11300    311      AI
Iceland       337000  17036      IS
Nauru         337000    182      NR

To order by the smallest values in column "population" and then "GDP", we can specify multiple columns like in the next example.

>>> df.nsmallest(3, ['population', 'GDP'])
          population  GDP alpha-2
Tuvalu         11300   38      TV
Anguilla       11300  311      AI
Nauru         337000  182      NR
method

swaplevel(i=-2, j=-1, axis=0)

Swap levels i and j in a :class:MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to swap levels on. 0 or 'index' for row-wise, 1 or'columns' for column-wise.
Returns (DataFrame)

DataFrame with levels swapped in MultiIndex.

Examples
>>> df = pd.DataFrame(...     {"Grade": ["A", "B", "A", "C"]},
...     index=[
...         ["Final exam", "Final exam", "Coursework", "Coursework"],
...         ["History", "Geography", "History", "Geography"],
...         ["January", "February", "March", "April"],
...     ],
... )
>>> df
                                    Grade
Final exam  History     January      A
            Geography   February     B
Coursework  History     March        A
            Geography   April        C

In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.

>>> df.swaplevel()
                                    Grade
Final exam  January     History         A
            February    Geography       B
Coursework  March       History         A
            April       Geography       C

By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.

>>> df.swaplevel(0)
                                    Grade
January     History     Final exam      A
February    Geography   Final exam      B
March       History     Coursework      A
April       Geography   Coursework      C

We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.

>>> df.swaplevel(0, 1)
                                    Grade
History     Final exam  January         A
Geography   Final exam  February        B
History     Coursework  March           A
Geography   Coursework  April           C
method

reorder_levels(order, axis=0)

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters
  • order (list of int or list of str) List representing new level order. Reference level by number(position) or by key (label).
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Where to reorder levels.
method

compare(other, align_axis=1, keep_shape=False, keep_equal=False)

Compare to another DataFrame and show the differences.

.. versionadded:: 1.1.0

Parameters
  • other (DataFrame) Object to compare with.
  • align_axis ({0 or 'index', 1 or 'columns'}, default 1) Determine which axis to align the comparison on.
    • 0, or 'index' : Resulting differences are stacked vertically with rows drawn alternately from self and other.
    • 1, or 'columns' : Resulting differences are aligned horizontally with columns drawn alternately from self and other.
  • keep_shape (bool, default False) If true, all rows and columns are kept.Otherwise, only the ones with different values are kept.
  • keep_equal (bool, default False) If true, the result keeps values that are equal.Otherwise, equal values are shown as NaNs.
Returns (DataFrame)

DataFrame that shows the differences stacked side by side.

The resulting index will be a MultiIndex with 'self' and 'other' stacked alternately at the inner level.

Raises
  • ValueError When the two DataFrames don't have identical labels or shape.
See Also

Series.compare : Compare with another Series and show differences.DataFrame.equals : Test whether two objects contain the same elements.

Notes

Matching NaNs will not appear as a difference.

Can only compare identically-labeled (i.e. same shape, identical row and column labels) DataFrames

Examples
>>> df = pd.DataFrame(...     {
...         "col1": ["a", "a", "b", "b", "a"],
...         "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
...     },
...     columns=["col1", "col2", "col3"],
... )
>>> df
  col1  col2  col3
0    a   1.0   1.0
1    a   2.0   2.0
2    b   3.0   3.0
3    b   NaN   4.0
4    a   5.0   5.0
>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
  col1  col2  col3
0    c   1.0   1.0
1    a   2.0   2.0
2    b   3.0   4.0
3    b   NaN   4.0
4    a   5.0   5.0

Align the differences on columns

>>> df.compare(df2)
  col1       col3
  self other self other
0    a     c  NaN   NaN
2  NaN   NaN  3.0   4.0

Stack the differences on rows

>>> df.compare(df2, align_axis=0)
        col1  col3
0 self     a   NaN
  other    c   NaN
2 self   NaN   3.0
  other  NaN   4.0

Keep the equal values

>>> df.compare(df2, keep_equal=True)
  col1       col3
  self other self other
0    a     c  1.0   1.0
2    b     b  3.0   4.0

Keep all original rows and columns

>>> df.compare(df2, keep_shape=True)
  col1       col2       col3
  self other self other self other
0    a     c  NaN   NaN  NaN   NaN
1  NaN   NaN  NaN   NaN  NaN   NaN
2  NaN   NaN  NaN   NaN  3.0   4.0
3  NaN   NaN  NaN   NaN  NaN   NaN
4  NaN   NaN  NaN   NaN  NaN   NaN

Keep all original rows and columns and also all original values

>>> df.compare(df2, keep_shape=True, keep_equal=True)
  col1       col2       col3
  self other self other self other
0    a     c  1.0   1.0  1.0   1.0
1    a     a  2.0   2.0  2.0   2.0
2    b     b  3.0   3.0  3.0   4.0
3    b     b  NaN   NaN  4.0   4.0
4    a     a  5.0   5.0  5.0   5.0
method

combine(other, func, fill_value=None, overwrite=True)

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters
  • other (DataFrame) The DataFrame to merge column-wise.
  • func (function) Function that takes two series as inputs and return a Series or ascalar. Used to merge the two dataframes column by columns.
  • fill_value (scalar value, default None) The value to fill NaNs with prior to passing any column to themerge func.
  • overwrite (bool, default True) If True, columns in self that do not exist in other will beoverwritten with NaNs.
Returns (DataFrame)

Combination of the provided DataFrames.

See Also

DataFrame.combinefirst : Combine two DataFrame objects and default to non-null values in frame calling the method.

Examples

Combine using a simple function that chooses the smaller column.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3

Example using a true element-wise combine function.

>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
   A  B
0  1  2
1  0  3

Using fill_value fills Nones prior to passing the column to the merge function.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
   A    B
0  0 -5.0
1  0  4.0

However, if the same element in both dataframes is None, that None is preserved

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
    A    B
0  0 -5.0
1  0  3.0

Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
     A    B     C
0  NaN  NaN   NaN
1  NaN  3.0 -10.0
2  NaN  3.0   1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
     A    B     C
0  0.0  NaN   NaN
1  0.0  3.0 -10.0
2  NaN  3.0   1.0

Demonstrating the preference of the passed in dataframe.

>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
   A    B   C
0  0.0  NaN NaN
1  0.0  3.0 NaN
2  NaN  3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
     A    B   C
0  0.0  NaN NaN
1  0.0  3.0 1.0
2  NaN  3.0 1.0
method

combine_first(other)

Update null elements with value in the same location in other.

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two.

Parameters
  • other (DataFrame) Provided DataFrame to use to fill null values.
Returns (DataFrame)

The result of combining the provided DataFrame with the other object.

See Also

DataFrame.combine : Perform series-wise operation on two DataFrames using a given function.

Examples
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0

Null values still persist if the location of that null value does not exist in other

>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
     A    B    C
0  NaN  4.0  NaN
1  0.0  3.0  1.0
2  NaN  3.0  1.0
method

update(other, join='left', overwrite=True, filter_func=None, errors='ignore')

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Parameters
  • other (DataFrame, or object coercible into a DataFrame) Should have at least one matching index/column labelwith the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.
  • join ({'left'}, default 'left') Only left join is implemented, keeping the index and columns of theoriginal object.
  • overwrite (bool, default True) How to handle non-NA values for overlapping keys:
    • True: overwrite original DataFrame's values with values from other.
    • False: only update values that are NA in the original DataFrame.
  • filter_func (callable(1d-array) -> bool 1d-array, optional) Can choose to replace values other than NA. Return True for valuesthat should be updated.
  • errors ({'raise', 'ignore'}, default 'ignore') If 'raise', will raise a ValueError if the DataFrame and otherboth contain non-NA data in the same place.
Raises
  • NotImplementedError
    • If join != 'left'
  • ValueError
    • When errors='raise' and there's overlapping non-NA data.
    • When errors is not either 'ignore' or 'raise'
See Also

dict.update : Similar method for dictionaries.DataFrame.merge : For column(s)-on-column(s) operations.

Examples
>>> df = pd.DataFrame({'A': [1, 2, 3],...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

The DataFrame's length does not increase as a result of the update, only values at matching index/column labels are updated.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

For Series, its name attribute must be set.

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  y
2  c  e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  x
1  b  d
2  c  e

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0
method

groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=<no_default>, observed=False, dropna=True)

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters
  • by (mapping, function, label, or list of labels) Used to determine the groups for the groupby.If by is a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see .align() method). If an ndarray is passed, the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Split along rows (0) or columns (1).
  • level (int, level name, or sequence of such, default None) If the axis is a MultiIndex (hierarchical), group by a particularlevel or levels.
  • as_index (bool, default True) For aggregated output, return object with group labels as theindex. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output.
  • sort (bool, default True) Sort group keys. Get better performance by turning this off.Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.
  • group_keys (bool, default True) When calling apply, add group keys to index to identify pieces.
  • squeeze (bool, default False) Reduce the dimensionality of the return type if possible,otherwise return a consistent type.
    .. deprecated:: 1.1.0
  • observed (bool, default False) This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
  • dropna (bool, default True) If True, and if group keys contain NA values, NA values togetherwith row/column will be dropped. If False, NA values will also be treated as the key in groups
    .. versionadded:: 1.1.0
Returns (DataFrameGroupBy)

Returns a groupby object that contains information about the groups.

See Also

resample : Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide <https://pandas.pydata.org/pandas-docs/stable/groupby.html>__ for more.

Examples
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True:

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5
>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4
>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0
method

pivot(index=None, columns=None, values=None)

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns. See the :ref:User Guide <reshaping> for more on reshaping.

Parameters
  • index (str or object or a list of str, optional) Column to use to make new frame's index. If None, usesexisting index.
    .. versionchanged:: 1.1.0 Also accept list of index names.
  • columns (str or object or a list of str) Column to use to make new frame's columns.
    .. versionchanged:: 1.1.0 Also accept list of columns names.
  • values (str, object or a list of the previous, optional) Column(s) to use for populating new frame's values. If notspecified, all remaining columns will be used and the result will have hierarchically indexed columns.
Returns (DataFrame)

Returns reshaped DataFrame.

Raises
  • ValueError When there are any index, columns combinations with multiplevalues. DataFrame.pivot_table when you need to aggregate.
See Also

DataFrame.pivottable : Generalization of pivot that can handle duplicate values for one index/column pair. DataFrame.unstack : Pivot based on the index values instead of a column. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.

Notes

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods.

Examples
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',...                            'two'],
...                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
...                    'baz': [1, 2, 3, 4, 5, 6],
...                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
    foo   bar  baz  zoo
0   one   A    1    x
1   one   B    2    y
2   one   C    3    z
3   two   A    4    q
4   two   B    5    w
5   two   C    6    t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar')['baz']
bar  A   B   C
foo
one  1   2   3
two  4   5   6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
      baz       zoo
bar   A  B  C   A  B  C
foo
one   1  2  3   x  y  z
two   4  5  6   q  w  t

You could also assign a list of column names or a list of index names.

>>> df = pd.DataFrame({
...        "lev1": [1, 1, 1, 2, 2, 2],
...        "lev2": [1, 1, 2, 1, 1, 2],
...        "lev3": [1, 2, 1, 2, 1, 2],
...        "lev4": [1, 2, 3, 4, 5, 6],
...        "values": [0, 1, 2, 3, 4, 5]})
>>> df
    lev1 lev2 lev3 lev4 values
0   1    1    1    1    0
1   1    1    2    2    1
2   1    2    1    3    2
3   2    1    2    4    3
4   2    1    1    5    4
5   2    2    2    6    5
>>> df.pivot(index="lev1", columns=["lev2", "lev3"],values="values")
lev2    1         2
lev3    1    2    1    2
lev1
1     0.0  1.0  2.0  NaN
2     4.0  3.0  NaN  5.0
>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"],values="values")
      lev3    1    2
lev1  lev2
   1     1  0.0  1.0
         2  2.0  NaN
   2     1  4.0  3.0
         2  NaN  5.0

A ValueError is raised if there are any duplicates.

>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
...                    "bar": ['A', 'A', 'B', 'C'],
...                    "baz": [1, 2, 3, 4]})
>>> df
   foo bar  baz
0  one   A    1
1  one   A    2
2  two   B    3
3  two   C    4

Notice that the first two rows are the same for our index and columns arguments.

>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
   ...
ValueError: Index contains duplicate entries, cannot reshape
method

pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True)

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Parameters
  • index (column, Grouper, array, or list of the previous) If an array is passed, it must be the same length as the data. Thelist can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.
  • columns (column, Grouper, array, or list of the previous) If an array is passed, it must be the same length as the data. Thelist can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.
  • aggfunc (function, list of functions, dict, default numpy.mean) If list of functions passed, the resulting pivot table will havehierarchical columns whose top level are the function names (inferred from the function objects themselves) If dict is passed, the key is column to aggregate and value is function or list of functions.
  • fill_value (scalar, default None) Value to replace missing values with (in the resulting pivot table,after aggregation).
  • margins (bool, default False) Add all row / columns (e.g. for subtotal / grand totals).
  • dropna (bool, default True) Do not include columns whose entries are all NaN.
  • margins_name (str, default 'All') Name of the row / column that will contain the totalswhen margins is True.
  • observed (bool, default False) This only applies if any of the groupers are Categoricals.If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.
    .. versionchanged:: 0.25.0
  • sort (bool, default True) Specifies if the result should be sorted.
    .. versionadded:: 1.3.0
Returns (DataFrame)

An Excel style pivot table.

See Also

DataFrame.pivot : Pivot without aggregation that can handle non-numeric data. DataFrame.melt: Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. wide_to_long : Wide panel to long format. Less flexible but more user-friendly than melt.

Examples
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
...                    "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9

This first example aggregates values by taking the sum.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum)
>>> table
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

We can also fill missing values using the fill_value parameter.

>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C        large  small
A   B
bar one      4      5
    two      7      6
foo one      4      1
    two      0      6

The next example aggregates by taking the mean across multiple columns.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': np.mean})
>>> table
                D         E
A   C
bar large  5.500000  7.500000
    small  5.500000  8.500000
foo large  2.000000  4.500000
    small  2.333333  4.333333

We can also calculate multiple types of aggregations for any given value column.

>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
...                     aggfunc={'D': np.mean,
...                              'E': [min, max, np.mean]})
>>> table
                D    E
            mean  max      mean  min
A   C
bar large  5.500000  9.0  7.500000  6.0
    small  5.500000  9.0  8.500000  8.0
foo large  2.000000  5.0  4.500000  4.0
    small  2.333333  6.0  4.333333  2.0
method

stack(level=-1, dropna=True)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

  • if the columns have a single level, the output is a Series;
  • if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.
Parameters
  • level (int, str, list, default -1) Level(s) to stack from the column axis onto the indexaxis, defined as one index or label, or a list of indices or labels.
  • dropna (bool, default True) Whether to drop rows in the resulting Frame/Series withmissing values. Stacking a column level onto the index axis can create combinations of index and column values that are missing from the original dataframe. See Examples section.
Returns (DataFrame or Series)

Stacked dataframe or series.

See Also

DataFrame.unstack : Unstack prescribed level(s) from index axis onto column axis. DataFrame.pivot : Reshape dataframe from long format to wide format. DataFrame.pivottable : Create a spreadsheet-style pivot table as a DataFrame.

Notes

The function is named by analogy with a collection of books being reorganized from being side by side on a horizontal position (the columns of the dataframe) to being stacked vertically on top of each other (in the index of the dataframe).

Examples

Single level columns

>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
...                                     index=['cat', 'dog'],
...                                     columns=['weight', 'height'])

Stacking a dataframe with a single level column axis returns a Series:

>>> df_single_level_cols
     weight height
cat       0      1
dog       2      3
>>> df_single_level_cols.stack()
cat  weight    0
     height    1
dog  weight    2
     height    3
dtype: int64

Multi level columns: simple case

>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol1)

Stacking a dataframe with a multi-level column axis:

>>> df_multi_level_cols1
     weight
         kg    pounds
cat       1        2
dog       2        4
>>> df_multi_level_cols1.stack()
            weight
cat kg           1
    pounds       2
dog kg           2
    pounds       4

Missing values

>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
...                                        ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

It is common to have missing values when stacking a dataframe with multi-level columns, as the stacked dataframe typically has more values than the original dataframe. Missing values are filled with NaNs:

>>> df_multi_level_cols2
    weight height
        kg      m
cat    1.0    2.0
dog    3.0    4.0
>>> df_multi_level_cols2.stack()
        height  weight
cat kg     NaN     1.0
    m      2.0     NaN
dog kg     NaN     3.0
    m      4.0     NaN

Prescribing the level(s) to be stacked

The first parameter controls which level or levels are stacked:

>>> df_multi_level_cols2.stack(0)
             kg    m
cat height  NaN  2.0
    weight  1.0  NaN
dog height  NaN  4.0
    weight  3.0  NaN
>>> df_multi_level_cols2.stack([0, 1])
cat  height  m     2.0
     weight  kg    1.0
dog  height  m     4.0
     weight  kg    3.0
dtype: float64

Dropping missing values

>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
...                                     index=['cat', 'dog'],
...                                     columns=multicol2)

Note that rows where all values are missing are dropped by default but this behaviour can be controlled via the dropna keyword parameter:

>>> df_multi_level_cols3
    weight height
        kg      m
cat    NaN    1.0
dog    2.0    3.0
>>> df_multi_level_cols3.stack(dropna=False)
        height  weight
cat kg     NaN     NaN
    m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
>>> df_multi_level_cols3.stack(dropna=True)
        height  weight
cat m      1.0     NaN
dog kg     NaN     2.0
    m      3.0     NaN
method

explode(column, ignore_index=False)

Transform each element of a list-like to a row, replicating index values.

.. versionadded:: 0.25.0

Parameters
  • column (str or tuple or list thereof) Column(s) to explode.For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.
    .. versionadded:: 1.3.0 Multi-column explode
  • ignore_index (bool, default False) If True, the resulting index will be labeled 0, 1, …, n - 1.
    .. versionadded:: 1.1.0
Returns (DataFrame)

Exploded lists to rows of the subset columns;index will be duplicated for these rows.

Raises
  • ValueError
    • If columns of the frame are not unique.
    • If specified columns to explode is empty list.
    • If specified columns to explode have not matching count of elements rowwise in the frame.
See Also

DataFrame.unstack : Pivot a level of the (necessarily hierarchical) index labels. DataFrame.melt : Unpivot a DataFrame from wide format to long format. Series.explode : Explode a DataFrame from list-like columns to long format.

Notes

This routine will explode list-likes including lists, tuples, sets, Series, and np.ndarray. The result dtype of the subset rows will be object. Scalars will be returned unchanged, and empty list-likes will result in a np.nan for that row. In addition, the ordering of rows in the output will be non-deterministic when exploding sets.

Examples
>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],...                    'B': 1,
...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
           A  B          C
0  [0, 1, 2]  1  [a, b, c]
1        foo  1        NaN
2         []  1         []
3     [3, 4]  1     [d, e]

Single-column explode.

>>> df.explode('A')
     A  B          C
0    0  1  [a, b, c]
0    1  1  [a, b, c]
0    2  1  [a, b, c]
1  foo  1        NaN
2  NaN  1         []
3    3  1     [d, e]
3    4  1     [d, e]

Multi-column explode.

>>> df.explode(list('AC'))
     A  B    C
0    0  1    a
0    1  1    b
0    2  1    c
1  foo  1  NaN
2  NaN  1  NaN
3    3  1    d
3    4  1    e
method

unstack(level=-1, fill_value=None)

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Parameters
  • level (int, str, or list of these, default -1 (last level)) Level(s) of index to unstack, can pass level name.
  • fill_value (int, str or dict) Replace NaN with this value if the unstack produces missing values.
See Also

DataFrame.pivot : Pivot a table based on column values.DataFrame.stack : Pivot a level of the column labels (inverse operation from unstack).

Examples
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),...                                    ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64
>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0
>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64
method

melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'.

Parameters
  • id_vars (tuple, list, or ndarray, optional) Column(s) to use as identifier variables.
  • value_vars (tuple, list, or ndarray, optional) Column(s) to unpivot. If not specified, uses all columns thatare not set as id_vars.
  • var_name (scalar) Name to use for the 'variable' column. If None it usesframe.columns.name or 'variable'.
  • value_name (scalar, default 'value') Name to use for the 'value' column.
  • col_level (int or str, optional) If columns are a MultiIndex then use this level to melt.
  • ignore_index (bool, default True) If True, original index is ignored. If False, the original index is retained.Index labels will be repeated as necessary.
    .. versionadded:: 1.1.0
Returns (DataFrame)

Unpivoted DataFrame.

See Also

melt : Identical method.pivot_table : Create a spreadsheet-style pivot table as a DataFrame. DataFrame.pivot : Return reshaped DataFrame organized by given index / column values. DataFrame.explode : Explode a DataFrame from list-like columns to long format.

Examples
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
3  a        C      2
4  b        C      4
5  c        C      6

The names of 'variable' and 'value' columns can be customized:

>>> df.melt(id_vars=['A'], value_vars=['B'],
...         var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

Original index values can be kept around:

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'], ignore_index=False)
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
0  a        C      2
1  b        C      4
2  c        C      6

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]
>>> df
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6
>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5
method

diff(periods=1, axis=0)

First discrete difference of element.

Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row).

Parameters
  • periods (int, default 1) Periods to shift for calculating difference, accepts negativevalues.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Take difference over rows (0) or columns (1).
Returns (Dataframe)

First differences of the Series.

See Also

Dataframe.pct_change: Percent change over given number of periods.Dataframe.shift: Shift index by desired number of periods with an optional time freq. Series.diff: First discrete difference of object.

Notes

For boolean dtypes, this uses :meth:operator.xor rather than :meth:operator.sub. The result is calculated according to current dtype in Dataframe, however dtype of the result is always float64.

Examples

w

, , ) f c 1 4 9 6 5 6

) c N 0 0 0 0 0

n

) c 0 3 7 3 0 8

w

) c N N N 0 0 0

w

) c 0 0 0 0 0 N

e

) ) a N 0

method

aggregate(func=None, axis=0, *args, **kwargs)

Aggregate using one or more operations over the specified axis.

Parameters
  • func (function, str, list or dict) Function to use for aggregating the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply.
    Accepted combinations are:
    • function
    • string function name
    • list of functions and/or function names, e.g. [np.sum, 'mean']
    • dict of axis labels -> functions, function names or list of such.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.
  • *args Positional arguments to pass to func.
  • **kwargs Keyword arguments to pass to func.
Returns (scalar, Series or DataFrame)

:

n n s

.

e m , d o .

.

See Also

DataFrame.apply : Perform any type of operations.DataFrame.transform : Perform transformation type operations. core.groupby.GroupBy : Perform operations over groups. core.resample.Resampler : Perform operations over resampled bins. core.window.Rolling : Perform operations over rolling window. core.window.Expanding : Perform operations over expanding window. core.window.ExponentialMovingWindow : Perform operation over exponential weighted window.

Notes

agg is an alias for aggregate. Use the alias.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

A passed user-defined-function will be passed a Series for evaluation.

Examples
>>> df = pd.DataFrame([[1, 2, 3],...                    [4, 5, 6],
...                    [7, 8, 9],
...                    [np.nan, np.nan, np.nan]],
...                   columns=['A', 'B', 'C'])

Aggregate these functions over the rows.

>>> df.agg(['sum', 'min'])
        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0

Different aggregations per column.

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
        A    B
sum  12.0  NaN
min   1.0  2.0
max   NaN  8.0

Aggregate different functions over the columns and rename the index of the resulting DataFrame.

>>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))
     A    B    C
x  7.0  NaN  NaN
y  NaN  2.0  NaN
z  NaN  NaN  6.0

Aggregate over the columns.

>>> df.agg("mean", axis="columns")
0    2.0
1    5.0
2    8.0
3    NaN
dtype: float64
method

transform(func, axis=0, *args, **kwargs)

Call func on self producing a DataFrame with transformed values.

Produced DataFrame will have same axis length as self.

Parameters
  • func (function, str, list-like or dict-like) Function to use for transforming the data. If a function, must eitherwork when passed a DataFrame or when passed to DataFrame.apply. If func is both list-like and dict-like, dict-like behavior takes precedence.
    Accepted combinations are:
    • function
    • string function name
    • list-like of functions and/or function names, e.g. [np.exp, 'sqrt']
    • dict-like of axis labels -> functions, function names or list-like of such.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index': apply function to each column.If 1 or 'columns': apply function to each row.
  • *args Positional arguments to pass to func.
  • **kwargs Keyword arguments to pass to func.
Returns (DataFrame)

A DataFrame that must have the same length as self.

See Also

DataFrame.agg : Only perform aggregating type operations.DataFrame.apply : Invoke function on a DataFrame.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

Examples
>>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)})>>> df
   A  B
0  0  1
1  1  2
2  2  3
>>> df.transform(lambda x: x + 1)
   A  B
0  1  2
1  2  3
2  3  4

Even though the resulting DataFrame must have the same length as the input DataFrame, it is possible to provide several input functions:

>>> s = pd.Series(range(3))
>>> s
0    0
1    1
2    2
dtype: int64
>>> s.transform([np.sqrt, np.exp])
       sqrt        exp
0  0.000000   1.000000
1  1.000000   2.718282
2  1.414214   7.389056

You can call transform on a GroupBy object:

>>> df = pd.DataFrame({
...     "Date": [
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05",
...         "2015-05-08", "2015-05-07", "2015-05-06", "2015-05-05"],
...     "Data": [5, 8, 6, 1, 50, 100, 60, 120],
... })
>>> df
         Date  Data
0  2015-05-08     5
1  2015-05-07     8
2  2015-05-06     6
3  2015-05-05     1
4  2015-05-08    50
5  2015-05-07   100
6  2015-05-06    60
7  2015-05-05   120
>>> df.groupby('Date')['Data'].transform('sum')
0     55
1    108
2     66
3    121
4     55
5    108
6     66
7    121
Name: Data, dtype: int64
>>> df = pd.DataFrame({
...     "c": [1, 1, 1, 2, 2, 2, 2],
...     "type": ["m", "n", "o", "m", "m", "n", "n"]
... })
>>> df
   c type
0  1    m
1  1    n
2  1    o
3  2    m
4  2    m
5  2    n
6  2    n
>>> df['size'] = df.groupby('c')['type'].transform(len)
>>> df
   c type size
0  1    m    3
1  1    n    3
2  1    o    3
3  2    m    4
4  2    m    4
5  2    n    4
6  2    n    4
method

apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame's index (axis=0) or the DataFrame's columns (axis=1). By default (result_type=None), the final return type is inferred from the return type of the applied function. Otherwise, it depends on the result_type argument.

Parameters
  • func (function) Function to apply to each column or row.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Axis along which the function is applied:
    • 0 or 'index': apply function to each column.
    • 1 or 'columns': apply function to each row.
  • raw (bool, default False) Determines if row or column is passed as a Series or ndarray object:
    • False : passes each row or column as a Series to the function.
    • True : the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance.
  • result_type ({'expand', 'reduce', 'broadcast', None}, default None) These only act when axis=1 (columns):
    • 'expand' : list-like results will be turned into columns.
    • 'reduce' : returns a Series if possible rather than expanding list-like results. This is the opposite of 'expand'.
    • 'broadcast' : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
    The default behaviour (None) depends on the return value of the applied function: list-like results will be returned as a Series of those. However if the apply function returns a Series these are expanded to columns.
  • args (tuple) Positional arguments to pass to func in addition to thearray/series.
  • **kwargs Additional keyword arguments to pass as keywords arguments tofunc.
Returns (Series or DataFrame)

Result of applying func along the given axis of theDataFrame.

See Also

DataFrame.applymap: For elementwise operations.DataFrame.aggregate: Only perform aggregating type operations. DataFrame.transform: Only perform transforming type operations.

Notes

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:gotchas.udf-mutation for more details.

Examples
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])>>> df
   A  B
0  4  9
1  4  9
2  4  9

Using a numpy universal function (in this case the same as np.sqrt(df)):

>>> df.apply(np.sqrt)
     A    B
0  2.0  3.0
1  2.0  3.0
2  2.0  3.0

Using a reducing function on either axis

>>> df.apply(np.sum, axis=0)
A    12
B    27
dtype: int64
>>> df.apply(np.sum, axis=1)
0    13
1    13
2    13
dtype: int64

Returning a list-like will result in a Series

>>> df.apply(lambda x: [1, 2], axis=1)
0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

Passing result_type='expand' will expand list-like results to columns of a Dataframe

>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
   0  1
0  1  2
1  1  2
2  1  2

Returning a Series inside the function is similar to passing result_type='expand'. The resulting column names will be the Series index.

>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
   foo  bar
0    1    2
1    1    2
2    1    2

Passing result_type='broadcast' will ensure the same shape result, whether list-like or scalar is returned by the function, and broadcast it along the axis. The resulting column names will be the originals.

>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
   A  B
0  1  2
1  1  2
2  1  2
method

applymap(func, na_action=None, **kwargs)

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Parameters
  • func (callable) Python function, returns a single value from a single value.
  • na_action ({None, 'ignore'}, default None) If ‘ignore’, propagate NaN values, without passing them to func.
    .. versionadded:: 1.2
  • **kwargs Additional keyword arguments to pass as keywords arguments tofunc.
    .. versionadded:: 1.3.0
Returns (DataFrame)

Transformed DataFrame.

See Also

DataFrame.apply : Apply a function along input axis of DataFrame.

Examples
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])>>> df
       0      1
0  1.000  2.120
1  3.356  4.567
>>> df.applymap(lambda x: len(str(x)))
   0  1
0  3  4
1  5  5

Like Series.map, NA values can be ignored:

>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')
      0  1
0  <NA>  4
1     5  5

Note that a vectorized version of func often exists, which will be much faster. You could square each number elementwise.

>>> df.applymap(lambda x: x**2)
           0          1
0   1.000000   4.494400
1  11.262736  20.857489

But it's better to avoid applymap in that case.

>>> df ** 2
           0          1
0   1.000000   4.494400
1  11.262736  20.857489
method

append(other, ignore_index=False, verify_integrity=False, sort=False)

Append rows of other to the end of caller, returning a new object.

Columns in other that are not in the caller are added as new columns.

Parameters
  • other (DataFrame or Series/dict-like object, or list of these) The data to append.
  • ignore_index (bool, default False) If True, the resulting axis will be labeled 0, 1, …, n - 1.
  • verify_integrity (bool, default False) If True, raise ValueError on creating index with duplicates.
  • sort (bool, default False) Sort columns if the columns of self and other are not aligned.
    .. versionchanged:: 1.0.0
    Changed to not sort by default.
    
Returns (DataFrame)

A new DataFrame consisting of the rows of caller and the rows of other.

See Also

concat : General function to concatenate DataFrame or Series objects.

Notes

If a list of dict/series is passed and the keys are all contained in the DataFrame's index, the order of the columns in the resulting DataFrame will be unchanged.

Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.

Examples
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'), index=['x', 'y'])>>> df
   A  B
x  1  2
y  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'), index=['x', 'y'])
>>> df.append(df2)
   A  B
x  1  2
y  3  4
x  5  6
y  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources.

Less efficient:

>>> df = pd.DataFrame(columns=['A'])
>>> for i in range(5):
...     df = df.append({'A': i}, ignore_index=True)
>>> df
   A
0  0
1  1
2  2
3  3
4  4

More efficient:

>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
...           ignore_index=True)
   A
0  0
1  1
2  2
3  3
4  4
method

join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)

Join columns of another DataFrame.

Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.

Parameters
  • other (DataFrame, Series, or list of DataFrame) Index should be similar to one of the columns in this one. If aSeries is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.
  • on (str, list of str, or array-like, optional) Column or index level name(s) in the caller to join on the indexin other, otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.
  • how ({'left', 'right', 'outer', 'inner'}, default 'left') How to handle the operation of the two objects.
    • left: use calling frame's index (or column if on is specified)
    • right: use other's index.
    • outer: form union of calling frame's index (or column if on is specified) with other's index, and sort it. lexicographically.
    • inner: form intersection of calling frame's index (or column if on is specified) with other's index, preserving the order of the calling's one.
  • lsuffix (str, default '') Suffix to use from left frame's overlapping columns.
  • rsuffix (str, default '') Suffix to use from right frame's overlapping columns.
  • sort (bool, default False) Order result DataFrame lexicographically by the join key. If False,the order of the join key depends on the join type (how keyword).
Returns (DataFrame)

A dataframe containing columns from both the caller and other.

See Also

DataFrame.merge : For column(s)-on-column(s) operations.

Notes

Parameters on, lsuffix, and rsuffix are not supported when passing a list of DataFrame objects.

Support for specifying index levels as the on parameter was added in version 0.23.0.

Examples
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],...                    'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
  key   A
0  K0  A0
1  K1  A1
2  K2  A2
3  K3  A3
4  K4  A4
5  K5  A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})
>>> other
  key   B
0  K0  B0
1  K1  B1
2  K2  B2

Join DataFrames using their indexes.

>>> df.join(other, lsuffix='_caller', rsuffix='_other')
  key_caller   A key_other    B
0         K0  A0        K0   B0
1         K1  A1        K1   B1
2         K2  A2        K2   B2
3         K3  A3       NaN  NaN
4         K4  A4       NaN  NaN
5         K5  A5       NaN  NaN

If we want to join using the key columns, we need to set key to be the index in both df and other. The joined DataFrame will have key as its index.

>>> df.set_index('key').join(other.set_index('key'))
      A    B
key
K0   A0   B0
K1   A1   B1
K2   A2   B2
K3   A3  NaN
K4   A4  NaN
K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other's index but we can use any column in df. This method preserves the original DataFrame's index in the result.

>>> df.join(other.set_index('key'), on='key')
  key   A    B
0  K0  A0   B0
1  K1  A1   B1
2  K2  A2   B2
3  K3  A3  NaN
4  K4  A4  NaN
5  K5  A5  NaN
method

merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

Merge DataFrame or named Series objects with a database-style join.

A named Series object is treated as a DataFrame with a single named column.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Parameters
  • right (DataFrame or named Series) Object to merge with.
  • how ({'left', 'right', 'outer', 'inner', 'cross'}, default 'inner') Type of merge to be performed.
    • left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
    • right: use only keys from right frame, similar to a SQL right outer join; preserve key order.
    • outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically.
    • inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
    • cross: creates the cartesian product from both frames, preserves the order of the left keys.
    .. versionadded:: 1.2.0
  • on (label or list) Column or index level names to join on. These must be found in bothDataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.
  • left_on (label or list, or array-like) Column or index level names to join on in the left DataFrame. Can alsobe an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.
  • right_on (label or list, or array-like) Column or index level names to join on in the right DataFrame. Can alsobe an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.
  • left_index (bool, default False) Use the index from the left DataFrame as the join key(s). If it is aMultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels.
  • right_index (bool, default False) Use the index from the right DataFrame as the join key. Same caveats asleft_index.
  • sort (bool, default False) Sort the join keys lexicographically in the result DataFrame. If False,the order of the join keys depends on the join type (how keyword).
  • suffixes (list-like, default is ("_x", "_y")) A length-2 sequence where each element is optionally a stringindicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.
  • copy (bool, default True) If False, avoid copy if possible.
  • indicator (bool or str, default False) If True, adds a column to the output DataFrame called "_merge" withinformation on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of "left_only" for observations whose merge key only appears in the left DataFrame, "right_only" for observations whose merge key only appears in the right DataFrame, and "both" if the observation's merge key is found in both DataFrames.
  • validate (str, optional) If specified, checks if merge is of specified type.
    • "one_to_one" or "1:1": check if merge keys are unique in both left and right datasets.
    • "one_to_many" or "1:m": check if merge keys are unique in left dataset.
    • "many_to_one" or "m:1": check if merge keys are unique in right dataset.
    • "many_to_many" or "m:m": allowed, but does not result in checks.
Returns (DataFrame)

A DataFrame of the two merged objects.

See Also

merge_ordered : Merge with optional filling/interpolation.merge_asof : Merge on nearest keys. DataFrame.join : Similar method using indices.

Notes

Support for specifying index levels as the on, left_on, and right_on parameters was added in version 0.23.0 Support for merging named Series objects was added in version 0.24.0

Examples
>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],...                     'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df1
    lkey value
0   foo      1
1   bar      2
2   baz      3
3   foo      5
>>> df2
    rkey value
0   foo      5
1   bar      6
2   baz      7
3   foo      8

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  foo        5  foo        5
3  foo        5  foo        8
4  bar        2  bar        6
5  baz        3  baz        7

Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',
...           suffixes=('_left', '_right'))
  lkey  value_left rkey  value_right
0  foo           1  foo            5
1  foo           1  foo            8
2  foo           5  foo            5
3  foo           5  foo            8
4  bar           2  bar            6
5  baz           3  baz            7

Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
    Index(['value'], dtype='object')
>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
      a  b
0   foo  1
1   bar  2
>>> df2
      a  c
0   foo  3
1   baz  4
>>> df1.merge(df2, how='inner', on='a')
      a  b  c
0   foo  1  3
>>> df1.merge(df2, how='left', on='a')
      a  b  c
0   foo  1  3.0
1   bar  2  NaN
>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
    left
0   foo
1   bar
>>> df2
    right
0   7
1   8
>>> df1.merge(df2, how='cross')
   left  right
0   foo      7
1   foo      8
2   bar      7
3   bar      8
method

round(decimals=0, *args, **kwargs)

Round a DataFrame to a variable number of decimal places.

Parameters
  • decimals (int, dict, Series) Number of decimal places to round each column to. If an int isgiven, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.
  • *args Additional keywords have no effect but might be accepted forcompatibility with numpy.
  • **kwargs Additional keywords have no effect but might be accepted forcompatibility with numpy.
Returns (DataFrame)

A DataFrame with the affected columns rounded to the specifiednumber of decimal places.

See Also

numpy.around : Round a numpy array to the given number of decimals.Series.round : Round a Series to the given number of decimals.

Examples
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],...                   columns=['dogs', 'cats'])
>>> df
    dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18

By providing an integer each column is rounded to the same number of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2

With a dict, the number of places for specific columns can be specified with the column names as key and the number of decimal places as value

>>> df.round({'dogs': 1, 'cats': 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0

Using a Series, the number of places for specific columns can be specified with the column names as index and the number of decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0
method

corr(method='pearson', min_periods=1)

Compute pairwise correlation of columns, excluding NA/null values.

Parameters
  • method ({'pearson', 'kendall', 'spearman'} or callable) Method of correlation:
    • pearson : standard correlation coefficient
    • kendall : Kendall Tau correlation coefficient
    • spearman : Spearman rank correlation
    • callable: callable with input two 1d ndarrays and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable's behavior.
  • min_periods (int, optional) Minimum number of observations required per pair of columnsto have a valid result.
Returns (DataFrame)

Correlation matrix.

See Also

DataFrame.corrwith : Compute pairwise correlation with another DataFrame or Series. Series.corr : Compute the correlation between two Series.

Examples
>>> def histogram_intersection(a, b):...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
...                   columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
      dogs  cats
dogs   1.0   0.3
cats   0.3   1.0
method

cov(min_periods=None, ddof=1)

Compute pairwise covariance of columns, excluding NA/null values.

Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix <https://en.wikipedia.org/wiki/Covariance_matrix>__ of the columns of the DataFrame.

Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.

This method is generally used for the analysis of time series data to understand the relationship between different measures across time.

Parameters
  • min_periods (int, optional) Minimum number of observations required per pair of columnsto have a valid result.
  • ddof (int, default 1) Delta degrees of freedom. The divisor used in calculationsis N - ddof, where N represents the number of elements.
    .. versionadded:: 1.1.0
Returns (DataFrame)

The covariance matrix of the series of the DataFrame.

See Also

Series.cov : Compute covariance with another Series.core.window.ExponentialMovingWindow.cov: Exponential weighted sample covariance. core.window.Expanding.cov : Expanding sample covariance. core.window.Rolling.cov : Rolling sample covariance.

Notes

Returns the covariance matrix of the DataFrame's time series. The covariance is normalized by N-ddof.

For DataFrames that have Series that are missing data (assuming that data is missing at random <https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>__) the returned covariance matrix will be an unbiased estimate of the variance and covariance between the member Series.

However, for many applications this estimate may not be acceptable because the estimate covariance matrix is not guaranteed to be positive semi-definite. This could lead to estimate correlations having absolute values which are greater than one, and/or a non-invertible covariance matrix. See Estimation of covariance matrices <https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_ matrices>__ for more details.

Examples
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],...                   columns=['dogs', 'cats'])
>>> df.cov()
          dogs      cats
dogs  0.666667 -1.000000
cats -1.000000  1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
...                   columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
          a         b         c         d         e
a  0.998438 -0.020161  0.059277 -0.008943  0.014144
b -0.020161  1.059352 -0.008543 -0.024738  0.009826
c  0.059277 -0.008543  1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486  0.921297 -0.013692
e  0.014144  0.009826 -0.000271 -0.013692  0.977795

Minimum number of periods

This method also supports an optional min_periods keyword that specifies the required minimum number of non-NA observations for each column pair in order to have a valid result:

>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
...                   columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
          a         b         c
a  0.316741       NaN -0.150812
b       NaN  1.248003  0.191417
c -0.150812  0.191417  0.895202
method

corrwith(other, axis=0, drop=False, method='pearson')

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Parameters
  • other (DataFrame, Series) Object with which to compute correlations.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' to compute column-wise, 1 or 'columns' forrow-wise.
  • drop (bool, default False) Drop missing indices from result.
  • method ({'pearson', 'kendall', 'spearman'} or callable) Method of correlation:
    • pearson : standard correlation coefficient
    • kendall : Kendall Tau correlation coefficient
    • spearman : Spearman rank correlation
    • callable: callable with input two 1d ndarrays and returning a float.
Returns (Series)

Pairwise correlations.

See Also

DataFrame.corr : Compute pairwise correlation of columns.

method

count(axis=0, level=None, numeric_only=False)

Count non-NA cells for each column or row.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) If 0 or 'index' counts are generated for each column.If 1 or 'columns' counts are generated for each row.
  • level (int or str, optional) If the axis is a MultiIndex (hierarchical), count along aparticular level, collapsing into a DataFrame. A str specifies the level name.
  • numeric_only (bool, default False) Include only float, int or boolean data.
Returns (Series or DataFrame)

For each column/row the number of non-NA/null entries.If level is specified returns a DataFrame.

See Also

Series.count: Number of non-NA elements in a Series.DataFrame.value_counts: Count unique combinations of columns. DataFrame.shape: Number of DataFrame rows and columns (including NA elements). DataFrame.isna: Boolean same-sized DataFrame showing places of NA elements.

Examples

Constructing DataFrame from a dictionary:

>>> df = pd.DataFrame({"Person":
...                    ["John", "Myla", "Lewis", "John", "Myla"],
...                    "Age": [24., np.nan, 21., 33, 26],
...                    "Single": [False, True, True, True, False]})
>>> df
   Person   Age  Single
0    John  24.0   False
1    Myla   NaN    True
2   Lewis  21.0    True
3    John  33.0    True
4    Myla  26.0   False

Notice the uncounted NA values:

>>> df.count()
Person    5
Age       4
Single    5
dtype: int64

Counts for each row:

>>> df.count(axis='columns')
0    3
1    2
2    3
3    3
4    3
dtype: int64
method

nunique(axis=0, dropna=True)

Count number of distinct elements in specified axis.

Return Series with number of distinct elements. Can ignore NaN values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' forcolumn-wise.
  • dropna (bool, default True) Don't include NaN in the counts.
See Also

Series.nunique: Method nunique for Series.DataFrame.count: Count non-NA cells for each column or row.

Examples
>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})>>> df.nunique()
A    3
B    2
dtype: int64
>>> df.nunique(axis=1)
0    1
1    2
2    2
dtype: int64
method

idxmin(axis=0, skipna=True)

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series)

Indexes of minima along the specified axis.

Raises
  • ValueError
    • If the row/column is empty
See Also

Series.idxmin : Return index of the minimum element.

Notes

This method is the DataFrame version of ndarray.argmin.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the minimum value in each column.

>>> df.idxmin()
consumption                Pork
co2_emissions    Wheat Products
dtype: object

To return the index for the minimum value in each row, use axis="columns".

>>> df.idxmin(axis="columns")
Pork                consumption
Wheat Products    co2_emissions
Beef                consumption
dtype: object
method

idxmax(axis=0, skipna=True)

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • skipna (bool, default True) Exclude NA/null values. If an entire row/column is NA, the resultwill be NA.
Returns (Series)

Indexes of maxima along the specified axis.

Raises
  • ValueError
    • If the row/column is empty
See Also

Series.idxmax : Return index of the maximum element.

Notes

This method is the DataFrame version of ndarray.argmax.

Examples

Consider a dataset containing food consumption in Argentina.

>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
...                    'co2_emissions': [37.2, 19.66, 1712]},
...                    index=['Pork', 'Wheat Products', 'Beef'])
>>> df
                consumption  co2_emissions
Pork                  10.51         37.20
Wheat Products       103.11         19.66
Beef                  55.48       1712.00

By default, it returns the index for the maximum value in each column.

>>> df.idxmax()
consumption     Wheat Products
co2_emissions             Beef
dtype: object

To return the index for the maximum value in each row, use axis="columns".

>>> df.idxmax(axis="columns")
Pork              co2_emissions
Wheat Products     consumption
Beef              co2_emissions
dtype: object
method

mode(axis=0, numeric_only=False, dropna=True)

Get the mode(s) of each element along the selected axis.

The mode of a set of values is the value that appears most often. It can be multiple values.

Parameters
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to iterate over while searching for the mode:
    • 0 or 'index' : get mode of each column
    • 1 or 'columns' : get mode of each row.
  • numeric_only (bool, default False) If True, only apply to numeric columns.
  • dropna (bool, default True) Don't consider counts of NaN/NaT.
Returns (DataFrame)

The modes of each column or row.

See Also

Series.mode : Return the highest frequency value in a Series.Series.value_counts : Return the counts of values in a Series.

Examples
>>> df = pd.DataFrame([('bird', 2, 2),...                    ('mammal', 4, np.nan),
...                    ('arthropod', 8, 0),
...                    ('bird', 2, np.nan)],
...                   index=('falcon', 'horse', 'spider', 'ostrich'),
...                   columns=('species', 'legs', 'wings'))
>>> df
           species  legs  wings
falcon        bird     2    2.0
horse       mammal     4    NaN
spider   arthropod     8    0.0
ostrich       bird     2    NaN

By default, missing values are not considered, and the mode of wings are both 0 and 2. Because the resulting DataFrame has two rows, the second row of species and legs contains NaN.

>>> df.mode()
  species  legs  wings
0    bird   2.0    0.0
1     NaN   NaN    2.0

Setting dropna=False NaN values are considered and they can be the mode (like for wings).

>>> df.mode(dropna=False)
  species  legs  wings
0    bird     2    NaN

Setting numeric_only=True, only the mode of numeric columns is computed, and columns of other types are ignored.

>>> df.mode(numeric_only=True)
   legs  wings
0   2.0    0.0
1   NaN    2.0

To compute the mode over columns and not rows, use the axis parameter:

>>> df.mode(axis='columns', numeric_only=True)
           0    1
falcon   2.0  NaN
horse    4.0  NaN
spider   0.0  8.0
ostrich  2.0  NaN
method

quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Return values at the given quantile over requested axis.

Parameters
  • q (float or array-like, default 0.5 (50% quantile)) Value between 0 <= q <= 1, the quantile(s) to compute.
  • axis ({0, 1, 'index', 'columns'}, default 0) Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
  • numeric_only (bool, default True) If False, the quantile of datetime and timedelta data will becomputed as well.
  • interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) This optional parameter specifies the interpolation method to use,when the desired quantile lies between two data points i and j:
    • linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
    • lower: i.
    • higher: j.
    • nearest: i or j whichever is nearest.
    • midpoint: (i + j) / 2.
Returns (Series or DataFrame)

ee . e .

See Also

core.window.Rolling.quantile: Rolling quantile.numpy.percentile: Numpy function to compute the percentile.

Examples
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),...                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

Specifying numeric_only=False will also compute the quantile of datetime and timedelta data.

>>> df = pd.DataFrame({'A': [1, 2],
...                    'B': [pd.Timestamp('2010'),
...                          pd.Timestamp('2011')],
...                    'C': [pd.Timedelta('1 days'),
...                          pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A                    1.5
B    2010-07-02 12:00:00
C        1 days 12:00:00
Name: 0.5, dtype: object
method

asfreq(freq, method=None, how=None, normalize=False, fill_value=None)

Convert time series to specified frequency.

Returns the original data conformed to a new index with the specified frequency.

If the index of this DataFrame is a :class:~pandas.PeriodIndex, the new index is the result of transforming the original index with :meth:PeriodIndex.asfreq <pandas.PeriodIndex.asfreq> (so the original index will map one-to-one to the new index).

Otherwise, the new index will be equivalent to pd.date_range(start, end, freq=freq) where start and end are, respectively, the first and last entries in the original index (see :func:pandas.date_range). The values corresponding to any timesteps in the new index which were not present in the original index will be null (NaN), unless a method for filling such unknowns is provided (see the method parameter below).

The :meth:resample method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency.

Parameters
  • freq (DateOffset or str) Frequency DateOffset or string.
  • method ({'backfill'/'bfill', 'pad'/'ffill'}, default None) Method to use for filling holes in reindexed Series (note thisdoes not fill NaNs that already were present):
    • 'pad' / 'ffill': propagate last valid observation forward to next valid
    • 'backfill' / 'bfill': use NEXT valid observation to fill.
  • how ({'start', 'end'}, default end) For PeriodIndex only (see PeriodIndex.asfreq).
  • normalize (bool, default False) Whether to reset output index to midnight.
  • fill_value (scalar, optional) Value to use for missing values, applied during upsampling (notethis does not fill NaNs that already were present).
Returns (DataFrame)

DataFrame object reindexed to the specified frequency.

See Also

reindex : Conform DataFrame to new index with optional filling logic.

Notes

To learn more about the frequency strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>__.

Examples

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s': series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30S', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30S', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0
method

resample(rule, axis=0, closed=None, label=None, convention='start', kind=None, loffset=None, base=None, on=None, level=None, origin='start_day', offset=None)

Resample time-series data.

Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or the caller must pass the label of a datetime-like series/index to the on/level keyword parameter.

Parameters
  • rule (DateOffset, Timedelta or str) The offset string or object representing target conversion.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) Which axis to use for up- or down-sampling. For Series thiswill default to 0, i.e. along the rows. Must be DatetimeIndex, TimedeltaIndex or PeriodIndex.
  • closed ({'right', 'left'}, default None) Which side of bin interval is closed. The default is 'left'for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'.
  • label ({'right', 'left'}, default None) Which bin edge label to label bucket with. The default is 'left'for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'.
  • convention ({'start', 'end', 's', 'e'}, default 'start') For PeriodIndex only, controls whether to use the start orend of rule.
  • kind ({'timestamp', 'period'}, optional, default None) Pass 'timestamp' to convert the resulting index to aDateTimeIndex or 'period' to convert it to a PeriodIndex. By default the input representation is retained.
  • loffset (timedelta, default None) Adjust the resampled time labels.
    .. deprecated:: 1.1.0 You should add the loffset to the df.index after the resample. See below.
  • base (int, default 0) For frequencies that evenly subdivide 1 day, the "origin" of theaggregated intervals. For example, for '5min' frequency, base could range from 0 through 4. Defaults to 0.
    .. deprecated:: 1.1.0 The new arguments that you should use are 'offset' or 'origin'.
  • on (str, optional) For a DataFrame, column to use instead of index for resampling.Column must be datetime-like.
  • level (str or int, optional) For a MultiIndex, level (name or number) to use forresampling. level must be datetime-like.
  • origin ({'epoch', 'start', 'start_day', 'end', 'end_day'}, Timestamp) or str, default 'start_day'The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a timestamp is not used, these values are also supported:
    • 'epoch': origin is 1970-01-01
    • 'start': origin is the first value of the timeseries
    • 'start_day': origin is the first day at midnight of the timeseries
    .. versionadded:: 1.1.0
    • 'end': origin is the last value of the timeseries
    • 'end_day': origin is the ceiling midnight of the last day
    .. versionadded:: 1.3.0
  • offset (Timedelta or str, default is None) An offset timedelta added to the origin.
    .. versionadded:: 1.1.0
Returns (pandas.core.Resampler)

:class:~pandas.core.Resampler object.

See Also

Series.resample : Resample a Series.DataFrame.resample : Resample a DataFrame. groupby : Group DataFrame by mapping, function, label, or list of labels. asfreq : Reindex a DataFrame with the given frequency without grouping.

Notes

See the user guide <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>__ for more.

To learn more about the offset strings, please see this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>__.

Examples

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: T, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample('3T').sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one.

>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but close the right side of the bin interval.

>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3T, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample('30S').asfreq()[0:5]   # Select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30S, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the pad method.

>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(arraylike):
...     return np.sum(arraylike) + 5
...
>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3T, dtype: int64

For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.

Resample a year by quarter using 'start' convention. Values are assigned to the first quarter of the period.

>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
...                                             freq='A',
...                                             periods=2))
>>> s
2012    1
2013    2
Freq: A-DEC, dtype: int64
>>> s.resample('Q', convention='start').asfreq()
2012Q1    1.0
2012Q2    NaN
2012Q3    NaN
2012Q4    NaN
2013Q1    2.0
2013Q2    NaN
2013Q3    NaN
2013Q4    NaN
Freq: Q-DEC, dtype: float64

Resample quarters by month using 'end' convention. Values are assigned to the last month of the period.

>>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',
...                                                   freq='Q',
...                                                   periods=4))
>>> q
2018Q1    1
2018Q2    2
2018Q3    3
2018Q4    4
Freq: Q-DEC, dtype: int64
>>> q.resample('M', convention='end').asfreq()
2018-03    1.0
2018-04    NaN
2018-05    NaN
2018-06    2.0
2018-07    NaN
2018-08    NaN
2018-09    3.0
2018-10    NaN
2018-11    NaN
2018-12    4.0
Freq: M, dtype: float64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...      'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
...                                     periods=8,
...                                     freq='W')
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
>>> df.resample('M', on='week_starting').mean()
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

For a DataFrame with MultiIndex, the keyword level can be used to specify on which level the resampling needs to take place.

>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
...       'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df2 = pd.DataFrame(
...     d2,
...     index=pd.MultiIndex.from_product(
...         [days, ['morning', 'afternoon']]
...     )
... )
>>> df2
                      price  volume
2000-01-01 morning       10      50
           afternoon     11      60
2000-01-02 morning        9      40
           afternoon     13     100
2000-01-03 morning       14      50
           afternoon     18     100
2000-01-04 morning       17      40
           afternoon     19      50
>>> df2.resample('D', level=0).sum()
            price  volume
2000-01-01     21     110
2000-01-02     22     140
2000-01-03     32     150
2000-01-04     36      90

If you want to adjust the start of the bins based on a fixed timestamp:

>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00     0
2000-10-01 23:37:00     3
2000-10-01 23:44:00     6
2000-10-01 23:51:00     9
2000-10-01 23:58:00    12
2000-10-02 00:05:00    15
2000-10-02 00:12:00    18
2000-10-02 00:19:00    21
2000-10-02 00:26:00    24
Freq: 7T, dtype: int64
>>> ts.resample('17min').sum()
2000-10-01 23:14:00     0
2000-10-01 23:31:00     9
2000-10-01 23:48:00    21
2000-10-02 00:05:00    54
2000-10-02 00:22:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00     0
2000-10-01 23:35:00    18
2000-10-01 23:52:00    27
2000-10-02 00:09:00    39
2000-10-02 00:26:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00     3
2000-10-01 23:41:00    15
2000-10-01 23:58:00    45
2000-10-02 00:15:00    45
Freq: 17T, dtype: int64

If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:

>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00     9
2000-10-01 23:47:00    21
2000-10-02 00:04:00    54
2000-10-02 00:21:00    24
Freq: 17T, dtype: int64

If you want to take the largest Timestamp as the end of the bins:

>>> ts.resample('17min', origin='end').sum()
2000-10-01 23:35:00     0
2000-10-01 23:52:00    18
2000-10-02 00:09:00    27
2000-10-02 00:26:00    63
Freq: 17T, dtype: int64

In contrast with the start_day, you can use end_day to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data:

>>> ts.resample('17min', origin='end_day').sum()
2000-10-01 23:38:00     3
2000-10-01 23:55:00    15
2000-10-02 00:12:00    45
2000-10-02 00:29:00    45
Freq: 17T, dtype: int64

To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2:

>>> ts.resample('17min', offset='2min').sum()
2000-10-01 23:16:00     0
2000-10-01 23:33:00     9
2000-10-01 23:50:00    36
2000-10-02 00:07:00    39
2000-10-02 00:24:00    24
Freq: 17T, dtype: int64

To replace the use of the deprecated loffset argument:

>>> from pandas.tseries.frequencies import to_offset
>>> loffset = '19min'
>>> ts_out = ts.resample('17min').sum()
>>> ts_out.index = ts_out.index + to_offset(loffset)
>>> ts_out
2000-10-01 23:33:00     0
2000-10-01 23:50:00     9
2000-10-02 00:07:00    21
2000-10-02 00:24:00    54
2000-10-02 00:41:00    24
Freq: 17T, dtype: int64
method

to_timestamp(freq=None, how='start', axis=0, copy=True)

Cast to DatetimeIndex of timestamps, at beginning of period.

Parameters
  • freq (str, default frequency of PeriodIndex) Desired frequency.
  • how ({'s', 'e', 'start', 'end'}) Convention for converting period to timestamp; start of periodvs. end.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to convert (the index by default).
  • copy (bool, default True) If False then underlying input data is not copied.
method

to_period(freq=None, axis=0, copy=True)

Convert DataFrame from DatetimeIndex to PeriodIndex.

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).

Parameters
  • freq (str, default) Frequency of the PeriodIndex.
  • axis ({0 or 'index', 1 or 'columns'}, default 0) The axis to convert (the index by default).
  • copy (bool, default True) If False then underlying input data is not copied.
method

isin(values)

Whether each element in the DataFrame is contained in values.

Parameters
  • values (iterable, Series, DataFrame or dict) The result will only be true at a location if all thelabels match. If values is a Series, that's the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.
Returns (DataFrame)

DataFrame of booleans showing whether each element in the DataFrameis contained in values.

See Also

DataFrame.eq: Equality test for DataFrame.Series.isin: Equivalent method on Series. Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index.

Examples
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},...                   index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that 'falcon' does not match based on the number of legs in df2.

>>> other = pd.DataFrame({'num_legs': [8, 2], 'num_wings': [0, 2]},
...                      index=['spider', 'falcon'])
>>> df.isin(other)
        num_legs  num_wings
falcon      True       True
dog        False      False
method

copy(deep=True, copy_grouped=False)DataFrameGroupByABC

Copy the dataframe and keep the class

Parameters
  • deep (bool, optional) Make a deep copy, including a copy of the data and the indices.With deep=False neither the indices nor the data are copied.
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